{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Linear Modeling in Python\n",
    "\n",
    "This notebook, which was created and presented by Skipper Sebold at the Scipy2012, introduces the use of pandas and the formula framework in statsmodels in the context of linear modeling.\n",
    "\n",
    "**It is based heavily on Jonathan Taylor's [class notes that use R](http://www.stanford.edu/class/stats191/interactions.html)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.graphics.api import interaction_plot, abline_plot, qqplot\n",
    "from statsmodels.stats.api import anova_lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Example 1: IT salary data\n",
    "\n",
    "In this example we will first establish a linear model, of salary as a function of experience, education, and management level.\n",
    "We will test for any interactions between these factors. Significant interactions will be included in the model.\n",
    "Finally, we will remove outliers, and plot the resulting fits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Get the data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Outcome:    S, salaries for IT staff in a corporation\n",
    "Predictors: X, experience in years\n",
    "            M, managment, 2 levels, 0=non-management, 1=management\n",
    "            E, education, 3 levels, 1=Bachelor's, 2=Master's, 3=Ph.D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "url = 'http://stats191.stanford.edu/data/salary.table'\n",
    "salary_table = pandas.read_table(url) # needs pandas 0.7.3\n",
    "#salary_table.to_csv('salary.table', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Inspect the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       S  X  E  M\n",
      "0  13876  1  1  1\n",
      "1  11608  1  3  0\n",
      "2  18701  1  3  1\n",
      "3  11283  1  2  0\n",
      "4  11767  1  3  0\n",
      "5  20872  2  2  1\n",
      "6  11772  2  2  0\n",
      "7  10535  2  1  0\n",
      "8  12195  2  3  0\n",
      "9  12313  3  2  0\n"
     ]
    }
   ],
   "source": [
    "print(salary_table.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "E = salary_table.E # Education\n",
    "M = salary_table.M # Management\n",
    "X = salary_table.X # Experience\n",
    "S = salary_table.S # Salary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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HDtmuXbvajz76yP7mN7+x1lp7yy232Iceeshaa+0777xjAbtnzx775Zdf2jp16tj169fb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\nOgAAgIoj6AAAACqOoAMAAKi4/w/clSgd5OSZLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8ae6be320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = fig.add_subplot(111, xlabel='Experience', ylabel='Salary',\n",
    "            xlim=(0, 27), ylim=(9600, 28800))\n",
    "symbols = ['D', '^']\n",
    "man_label = [\"Non-Mgmt\", \"Mgmt\"]\n",
    "educ_label = [\"Bachelors\", \"Masters\", \"PhD\"]\n",
    "colors = ['r', 'g', 'blue']\n",
    "factor_groups = salary_table.groupby(['E','M'])\n",
    "for values, group in factor_groups:\n",
    "    i,j = values\n",
    "    label = \"%s - %s\" % (man_label[j], educ_label[i-1])\n",
    "    ax.scatter(group['X'], group['S'], marker=symbols[j], color=colors[i-1],\n",
    "               s=350, label=label)\n",
    "ax.legend(scatterpoints=1, markerscale=.7, labelspacing=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Define and Fit a Linear Model\n",
    "\n",
    "Fit a linear model\n",
    "\n",
    "$$S_i = \\beta_0 + \\beta_1X_i + \\beta_2E_{i2} + \\beta_3E_{i3} + \\beta_4M_i + \\epsilon_i$$\n",
    "\n",
    "where\n",
    "\n",
    "$$ E_{i2}=\\cases{1,&if $E_i=2$;\\cr 0,&otherwise. \\cr}$$ \n",
    "$$ E_{i3}=\\cases{1,&if $E_i=3$;\\cr 0,&otherwise. \\cr}$$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In the following, the model is defined using \"patsy\".\n",
    "\n",
    "- An intercept is automatically included.\n",
    "- C(variable) includes the variable automatically as categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.957\n",
      "Model:                            OLS   Adj. R-squared:                  0.953\n",
      "Method:                 Least Squares   F-statistic:                     226.8\n",
      "Date:                Sat, 04 Feb 2017   Prob (F-statistic):           2.23e-27\n",
      "Time:                        17:08:50   Log-Likelihood:                -381.63\n",
      "No. Observations:                  46   AIC:                             773.3\n",
      "Df Residuals:                      41   BIC:                             782.4\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   8035.5976    386.689     20.781      0.000    7254.663    8816.532\n",
      "C(E)[T.2]   3144.0352    361.968      8.686      0.000    2413.025    3875.045\n",
      "C(E)[T.3]   2996.2103    411.753      7.277      0.000    2164.659    3827.762\n",
      "C(M)[T.1]   6883.5310    313.919     21.928      0.000    6249.559    7517.503\n",
      "X            546.1840     30.519     17.896      0.000     484.549     607.819\n",
      "==============================================================================\n",
      "Omnibus:                        2.293   Durbin-Watson:                   2.237\n",
      "Prob(Omnibus):                  0.318   Jarque-Bera (JB):                1.362\n",
      "Skew:                          -0.077   Prob(JB):                        0.506\n",
      "Kurtosis:                       2.171   Cond. No.                         33.5\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "formula = 'S ~ C(E) + C(M) + X'\n",
    "lm = ols(formula, salary_table).fit()\n",
    "print(lm.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "*Dep. Variable*\n",
    "    the variable to be fitted; here, the \"Salary\"\n",
    "\n",
    "*Model*\n",
    "    OLS = ordinary least squares\n",
    "\n",
    "*Df Residuals*\n",
    "    The number of observations, minus the number of parameters fitted.\n",
    "\n",
    "*Df model*\n",
    "    \"Degree of Freedom\" of the model, i.e. the dimensionnality of the subspace spanned by the model. This entails that the intercept is not counted.\n",
    "\n",
    "*R-squared*\n",
    "    Coefficient of Determination = (S0-Sm)/S0\n",
    "\n",
    "*Adj. R-squared*\n",
    "    The adjusted R2 coefficient, which takes into consideration the number of model paramters.\n",
    "\n",
    "*F-statistic*, and corresponding *Prob*\n",
    "    F-test on the regression model, if it is significantly different from the minimum model (i.e. a constant offset only)\n",
    "\n",
    "*Log-Likelihood*\n",
    "    Maximum log-likelihood value for the model.\n",
    "\n",
    "*AIC*\n",
    "    Aiken's Information Criterion, for the assessment of the model.\n",
    "\n",
    "*BIC*\n",
    "    Bayesian Information Criterion, for the assessment of the model.\n",
    "\n",
    "The values in the lowest box describe properties of the residuals (\"Skew\", \"Kurtosisi\"), as well as tests on the residuals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Inspect the Design Matrix, and demonstrate the Model Predictions\n",
    "\n",
    "Look at the design matrix created for us. Every results instance has a reference to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.,  1.,  1.],\n",
       "       [ 1.,  0.,  1.,  0.,  1.],\n",
       "       [ 1.,  0.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  0.,  0.,  1.],\n",
       "       [ 1.,  0.,  1.,  0.,  1.],\n",
       "       [ 1.,  1.,  0.,  1.,  2.],\n",
       "       [ 1.,  1.,  0.,  0.,  2.],\n",
       "       [ 1.,  0.,  0.,  0.,  2.],\n",
       "       [ 1.,  0.,  1.,  0.,  2.],\n",
       "       [ 1.,  1.,  0.,  0.,  3.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.model.exog[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Since we initially passed in a DataFrame, we have a transformed DataFrame available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Intercept  C(E)[T.2]  C(E)[T.3]  C(M)[T.1]    X\n",
      "0        1.0        0.0        0.0        1.0  1.0\n",
      "1        1.0        0.0        1.0        0.0  1.0\n",
      "2        1.0        0.0        1.0        1.0  1.0\n",
      "3        1.0        1.0        0.0        0.0  1.0\n",
      "4        1.0        0.0        1.0        0.0  1.0\n",
      "5        1.0        1.0        0.0        1.0  2.0\n",
      "6        1.0        1.0        0.0        0.0  2.0\n",
      "7        1.0        0.0        0.0        0.0  2.0\n",
      "8        1.0        0.0        1.0        0.0  2.0\n",
      "9        1.0        1.0        0.0        0.0  3.0\n"
     ]
    }
   ],
   "source": [
    "print(lm.model.data.orig_exog.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "There is a reference to the original untouched data in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       S  X  E  M\n",
      "0  13876  1  1  1\n",
      "1  11608  1  3  0\n",
      "2  18701  1  3  1\n",
      "3  11283  1  2  0\n",
      "4  11767  1  3  0\n",
      "5  20872  2  2  1\n",
      "6  11772  2  2  0\n",
      "7  10535  2  1  0\n",
      "8  12195  2  3  0\n",
      "9  12313  3  2  0\n"
     ]
    }
   ],
   "source": [
    "print(lm.model.data.frame.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If you use the formula interface, statsmodels remembers this transformation. Say you want to know the predicted salary for someone with 12 years experience and a Master's degree who is in a management position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24617.372072\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.predict(pd.DataFrame({'X' : [12], 'M' : [1], 'E' : [2]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Check the Residuals\n",
    "\n",
    "So far we've assumed that the effect of experience is the same for each level of education and professional role.\n",
    "Perhaps this assumption isn't merited. We can formally test this using some interactions.\n",
    "\n",
    "We can start by seeing if our model assumptions are met. Let's look at a residuals plot, with the groups separated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "resid = lm.resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k6cjMvvPnbJxj3q1GR0fZuHGAsbH3HzY+NvZ+Nm4c6JoVWupdlWUj8CvAXcBm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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8ae986e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "xticks = []\n",
    "ax = fig.add_subplot(111, xlabel='Group (E, M)', ylabel='Residuals')\n",
    "for values, group in factor_groups:\n",
    "    i,j = values\n",
    "    xticks.append(str((i, j)))\n",
    "    group_num = i*2 + j - 1 # for plotting purposes\n",
    "    x = [group_num] * len(group)\n",
    "    ax.scatter(x, resid[group.index], marker=symbols[j], color=colors[i-1],\n",
    "            s=144, edgecolors='black')\n",
    "ax.set_xticks([1,2,3,4,5,6])\n",
    "ax.set_xticklabels(xticks)\n",
    "ax.axis('tight');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Obviously, the linear model alone does not capture all model features, as the residuals are not normally distributed within each group. To improve the model, we check if interactions between the model parameters can explain the group differences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Add an Interaction\n",
    "\n",
    "#### Interaction Salary*Experience\n",
    "\n",
    "Add an interaction between salary and experience, allowing different intercepts for level of experience.\n",
    "\n",
    "$$S_i = \\beta_0+\\beta_1X_i+\\beta_2E_{i2}+\\beta_3E_{i3}+\\beta_4M_i+\\beta_5E_{i2}X_i+\\beta_6E_{i3}X_i+\\epsilon_i$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.961\n",
      "Model:                            OLS   Adj. R-squared:                  0.955\n",
      "Method:                 Least Squares   F-statistic:                     158.6\n",
      "Date:                Sat, 04 Feb 2017   Prob (F-statistic):           8.23e-26\n",
      "Time:                        17:09:30   Log-Likelihood:                -379.47\n",
      "No. Observations:                  46   AIC:                             772.9\n",
      "Df Residuals:                      39   BIC:                             785.7\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "Intercept    7256.2800    549.494     13.205      0.000    6144.824    8367.736\n",
      "C(E)[T.2]    4172.5045    674.966      6.182      0.000    2807.256    5537.753\n",
      "C(E)[T.3]    3946.3649    686.693      5.747      0.000    2557.396    5335.333\n",
      "C(M)[T.1]    7102.4539    333.442     21.300      0.000    6428.005    7776.903\n",
      "X             632.2878     53.185     11.888      0.000     524.710     739.865\n",
      "C(E)[T.2]:X  -125.5147     69.863     -1.797      0.080    -266.826      15.796\n",
      "C(E)[T.3]:X  -141.2741     89.281     -1.582      0.122    -321.861      39.313\n",
      "==============================================================================\n",
      "Omnibus:                        0.432   Durbin-Watson:                   2.179\n",
      "Prob(Omnibus):                  0.806   Jarque-Bera (JB):                0.590\n",
      "Skew:                           0.144   Prob(JB):                        0.744\n",
      "Kurtosis:                       2.526   Cond. No.                         69.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "interX_lm = ols('S ~ C(E)*X + C(M)', salary_table).fit()\n",
    "print(interX_lm.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The \"Factor\" Experience has the \"Treatments\" (or elements) \"1\", \"2\", and \"3\". Since the \"Treatment 1\"([T.1]) is taken as the reference, it is not listed here explicitly. This is called \"corner-point\" approach.\n",
    "Similarly, the \"Factor\" Management has the \"Treatments\" \"0\" and \"1\". With \"0\" taken as the reference, only the term C(M)[T.1] is listed in the model.\n",
    "The interactions are described by the terms \"C(E)[T.i]:X\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##### Test the Interaction Management*Experience\n",
    "\n",
    "Test that $\\beta_5 = \\beta_6 = 0$. We can use anova_lm or we can use an F-test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid           ssr  df_diff       ss_diff         F    Pr(>F)\n",
      "0      41.0  4.328072e+07      0.0           NaN       NaN       NaN\n",
      "1      39.0  3.941068e+07      2.0  3.870040e+06  1.914856  0.160964\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1814: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    }
   ],
   "source": [
    "print(anova_lm(lm, interX_lm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<F test: F=array([[ 1.91485593]]), p=0.16096422424743717, df_denom=39, df_num=2>\n"
     ]
    }
   ],
   "source": [
    "print(interX_lm.f_test('C(E)[T.2]:X = C(E)[T.3]:X = 0'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<F test: F=array([[ 1.91485593]]), p=0.16096422424743717, df_denom=39, df_num=2>\n"
     ]
    }
   ],
   "source": [
    "print(interX_lm.f_test([[0,0,0,0,0,1,-1],[0,0,0,0,0,0,1]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Both tests show that the models are not significantly different. In other words, there is no interaction effect between Management and Experience in the data.\n",
    "\n",
    "The contrasts are created here under the hood by patsy.\n",
    "\n",
    "Recall that F-tests are of the form $R\\beta = q$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0.  0.  0.  1. -1.]\n",
      " [ 0.  0.  0.  0.  0.  0.  1.]]\n",
      "[[ 0.]\n",
      " [ 0.]]\n"
     ]
    }
   ],
   "source": [
    "LC = interX_lm.model.data.orig_exog.design_info.linear_constraint('C(E)[T.2]:X = C(E)[T.3]:X = 0')\n",
    "print(LC.coefs)\n",
    "print(LC.constants)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Interact education with management"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.999\n",
      "Model:                            OLS   Adj. R-squared:                  0.999\n",
      "Method:                 Least Squares   F-statistic:                     5517.\n",
      "Date:                Sat, 04 Feb 2017   Prob (F-statistic):           1.67e-55\n",
      "Time:                        17:09:31   Log-Likelihood:                -298.74\n",
      "No. Observations:                  46   AIC:                             611.5\n",
      "Df Residuals:                      39   BIC:                             624.3\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept            9472.6854     80.344    117.902      0.000    9310.175    9635.196\n",
      "C(E)[T.2]            1381.6706     77.319     17.870      0.000    1225.279    1538.063\n",
      "C(E)[T.3]            1730.7483    105.334     16.431      0.000    1517.690    1943.806\n",
      "C(M)[T.1]            3981.3769    101.175     39.351      0.000    3776.732    4186.022\n",
      "C(E)[T.2]:C(M)[T.1]  4902.5231    131.359     37.322      0.000    4636.825    5168.222\n",
      "C(E)[T.3]:C(M)[T.1]  3066.0351    149.330     20.532      0.000    2763.986    3368.084\n",
      "X                     496.9870      5.566     89.283      0.000     485.728     508.246\n",
      "==============================================================================\n",
      "Omnibus:                       74.761   Durbin-Watson:                   2.244\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1037.873\n",
      "Skew:                          -4.103   Prob(JB):                    4.25e-226\n",
      "Kurtosis:                      24.776   Cond. No.                         79.0\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "interM_lm = ols('S ~ X + C(E)*C(M)', salary_table).fit()\n",
    "print(interM_lm.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid           ssr  df_diff       ss_diff           F        Pr(>F)\n",
      "0      41.0  4.328072e+07      0.0           NaN         NaN           NaN\n",
      "1      39.0  1.178168e+06      2.0  4.210255e+07  696.844466  3.025504e-31\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1814: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    }
   ],
   "source": [
    "print(anova_lm(lm, interM_lm))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The interaction effect Management*Education is highly significant!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##### Check the residuals of the extended model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "infl = interM_lm.get_influence()\n",
    "resid = infl.resid_studentized_internal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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JDUQrlUp6cMe9euqR90l2hvYP1BVJo5LGGufNgTbePtNFmh4u+OBwSa0253aX1GqF+GCR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8ae7af438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='X', ylabel='standardized resids')\n",
    "\n",
    "for values, group in factor_groups:\n",
    "    i,j = values\n",
    "    idx = group.index\n",
    "    ax.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],\n",
    "            s=144, edgecolors='black')\n",
    "ax.axis('tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "There looks to be an outlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Find and Remove the Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    student_resid       unadj_p     fdr_bh(p)\n",
      "32     -14.950832  1.676948e-17  7.713960e-16\n",
      "33       1.288001  2.055339e-01  9.599254e-01\n",
      "23       1.023194  3.126860e-01  9.599254e-01\n",
      "41       0.942329  3.519767e-01  9.599254e-01\n",
      "11       0.896574  3.755915e-01  9.599254e-01\n",
      "5        0.890643  3.787254e-01  9.599254e-01\n",
      "39       0.835081  4.088920e-01  9.599254e-01\n",
      "38       0.819164  4.178011e-01  9.599254e-01\n",
      "21       0.729762  4.700104e-01  9.599254e-01\n",
      "20      -0.726132  4.722071e-01  9.599254e-01\n",
      "30       0.704477  4.854309e-01  9.599254e-01\n",
      "28       0.603122  5.500109e-01  9.599254e-01\n",
      "44      -0.599720  5.522526e-01  9.599254e-01\n",
      "1       -0.590487  5.583601e-01  9.599254e-01\n",
      "17      -0.564832  5.755078e-01  9.599254e-01\n",
      "0       -0.482474  6.322368e-01  9.599254e-01\n",
      "34      -0.474793  6.376517e-01  9.599254e-01\n",
      "6       -0.464031  6.452728e-01  9.599254e-01\n",
      "7        0.428795  6.704934e-01  9.599254e-01\n",
      "45      -0.426443  6.721915e-01  9.599254e-01\n",
      "4        0.424446  6.736338e-01  9.599254e-01\n",
      "3       -0.418531  6.779148e-01  9.599254e-01\n",
      "35       0.382327  7.043486e-01  9.599254e-01\n",
      "43       0.360696  7.203240e-01  9.599254e-01\n",
      "12       0.357944  7.223662e-01  9.599254e-01\n",
      "37       0.342548  7.338259e-01  9.599254e-01\n",
      "2       -0.291688  7.721113e-01  9.599254e-01\n",
      "24       0.287114  7.755852e-01  9.599254e-01\n",
      "31      -0.285085  7.771273e-01  9.599254e-01\n",
      "36      -0.275068  7.847542e-01  9.599254e-01\n",
      "13      -0.268726  7.895942e-01  9.599254e-01\n",
      "27      -0.259790  7.964281e-01  9.599254e-01\n",
      "15       0.252085  8.023339e-01  9.599254e-01\n",
      "16       0.249972  8.039551e-01  9.599254e-01\n",
      "42       0.195876  8.457513e-01  9.599254e-01\n",
      "9       -0.194723  8.466475e-01  9.599254e-01\n",
      "10       0.190826  8.496777e-01  9.599254e-01\n",
      "26      -0.165977  8.690547e-01  9.599254e-01\n",
      "19       0.165168  8.696870e-01  9.599254e-01\n",
      "25      -0.161711  8.723902e-01  9.599254e-01\n",
      "14       0.147997  8.831273e-01  9.599254e-01\n",
      "29       0.147288  8.836831e-01  9.599254e-01\n",
      "40      -0.129912  8.973215e-01  9.599254e-01\n",
      "18      -0.072460  9.426159e-01  9.854621e-01\n",
      "22       0.016188  9.871691e-01  9.878792e-01\n",
      "8       -0.015292  9.878792e-01  9.878792e-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\ipykernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "outl = interM_lm.outlier_test('fdr_bh')\n",
    "outl.sort('unadj_p', inplace=True)\n",
    "print(outl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "idx = salary_table.index.drop(32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
      "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34,\n",
      "            35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45],\n",
      "           dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "print(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##### Rerun the original linear model without the outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.955\n",
      "Model:                            OLS   Adj. R-squared:                  0.950\n",
      "Method:                 Least Squares   F-statistic:                     211.7\n",
      "Date:                Sat, 04 Feb 2017   Prob (F-statistic):           2.45e-26\n",
      "Time:                        17:09:31   Log-Likelihood:                -373.79\n",
      "No. Observations:                  45   AIC:                             757.6\n",
      "Df Residuals:                      40   BIC:                             766.6\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   8044.7518    392.781     20.482      0.000    7250.911    8838.592\n",
      "C(E)[T.2]   3129.5286    370.470      8.447      0.000    2380.780    3878.277\n",
      "C(E)[T.3]   2999.4451    416.712      7.198      0.000    2157.238    3841.652\n",
      "C(M)[T.1]   6866.9856    323.991     21.195      0.000    6212.175    7521.796\n",
      "X            545.7855     30.912     17.656      0.000     483.311     608.260\n",
      "==============================================================================\n",
      "Omnibus:                        2.511   Durbin-Watson:                   2.265\n",
      "Prob(Omnibus):                  0.285   Jarque-Bera (JB):                1.400\n",
      "Skew:                          -0.044   Prob(JB):                        0.496\n",
      "Kurtosis:                       2.140   Cond. No.                         33.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm32 = ols('S ~ C(E) + X + C(M)', data=salary_table, subset=idx).fit()\n",
    "print(lm32.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Interaction Education*Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.959\n",
      "Model:                            OLS   Adj. R-squared:                  0.952\n",
      "Method:                 Least Squares   F-statistic:                     147.7\n",
      "Date:                Sat, 04 Feb 2017   Prob (F-statistic):           8.97e-25\n",
      "Time:                        17:09:32   Log-Likelihood:                -371.70\n",
      "No. Observations:                  45   AIC:                             757.4\n",
      "Df Residuals:                      38   BIC:                             770.0\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "Intercept    7266.0887    558.872     13.001      0.000    6134.711    8397.466\n",
      "C(E)[T.2]    4162.0846    685.728      6.070      0.000    2773.900    5550.269\n",
      "C(E)[T.3]    3940.4359    696.067      5.661      0.000    2531.322    5349.549\n",
      "C(M)[T.1]    7088.6387    345.587     20.512      0.000    6389.035    7788.243\n",
      "X             631.6892     53.950     11.709      0.000     522.473     740.905\n",
      "C(E)[T.2]:X  -125.5009     70.744     -1.774      0.084    -268.714      17.712\n",
      "C(E)[T.3]:X  -139.8410     90.728     -1.541      0.132    -323.511      43.829\n",
      "==============================================================================\n",
      "Omnibus:                        0.617   Durbin-Watson:                   2.194\n",
      "Prob(Omnibus):                  0.734   Jarque-Bera (JB):                0.728\n",
      "Skew:                           0.162   Prob(JB):                        0.695\n",
      "Kurtosis:                       2.468   Cond. No.                         68.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "interX_lm32 = ols('S ~ C(E) * X + C(M)', data=salary_table, subset=idx).fit()\n",
    "print(interX_lm32.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid           ssr  df_diff       ss_diff         F    Pr(>F)\n",
      "0      40.0  4.320910e+07      0.0           NaN       NaN       NaN\n",
      "1      38.0  3.937424e+07      2.0  3.834859e+06  1.850508  0.171042\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1814: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    }
   ],
   "source": [
    "table3 = anova_lm(lm32, interX_lm32)\n",
    "print(table3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Again, this is not significant, and can be left away."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Final Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Result from the Interaction Test: the significant model with Interaction Experience*Management, without the outlier #32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid           ssr  df_diff       ss_diff            F        Pr(>F)\n",
      "0      40.0  4.320910e+07      0.0           NaN          NaN           NaN\n",
      "1      38.0  1.711881e+05      2.0  4.303791e+07  4776.734853  2.291239e-46\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1814: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    }
   ],
   "source": [
    "interM_lm32 = ols('S ~ X + C(E) * C(M)', data=salary_table, subset=idx).fit()\n",
    "print(anova_lm(lm32, interM_lm32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Re-plotting the residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MYzsoFjW/tKSgWCS6u4yfLqCXEdwAgL7TdnR3KbaPlcvaLOlYuUx0dxE/XUCvI7gBAH2p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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8aecdc400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resid = interM_lm32.get_influence().summary_frame()['standard_resid']\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='X[~[32]]', ylabel='standardized resids')\n",
    "\n",
    "for values, group in factor_groups:\n",
    "    i,j = values\n",
    "    idx = group.index\n",
    "    ax.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],\n",
    "            s=144, edgecolors='black')\n",
    "ax.axis('tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A final plot of the fitted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dc8UfquP5XKqSDx8O48bBtm1Qs6bzcOYtt8D555daCCIiIaUKuYiEnU17NjF1\nhTP3LjIikrmb5tI2ri0Tek9g69+38r9e/wvbZBwKqY7nK6Uq+dq18Oabh99v2uT0hU+d6vSGv/mm\nknERCV+qkItIubX74G6mLJ+CJ83DnA1zqBRZiR3Dd1Atphr+gJ/IiEi3QywTjlodzxeiKvm2bTB5\nsjMvfP58iIiAzExISHCmpJTz6ZEiIkVShVxEKryxi8YS/1w8Qz4cwtb9W0m5NIXlf1t+aEyhkvHD\njlodzxeCKvnkyVCvHtx9N+TkwDPPwIYNTjIOSsZFRApShVxEyjx/wM+cDXPwLPFwc7ub6digIz9n\n/cw7v7zDwLYDaRffrtxv1hMqx6yO5zuBKnluLsya5VTC+/SB3r2dlpQxY5wpKa1andhnEBEpjzSH\nXETKPWstP2f9jCfNw8SlE9mybwvVY6pzYYML6digI2fFn8VZ8We5HWaZd8zqeL7qJZtLbi3Mm+ck\n4ZMnw44dUKsWdOzoHK9fH0aOPPH4RUTCgSrkIlKmHMg9QJXoKhzMPUjdZ+uSnZfNX07/CwPbDqRH\n8x7ERhexNaP8QVLDJDZv3Fzs8xMbJJK5IfOox3fudGaFBwLQqBFs3w49esDAgdCtG8TEnISgRUQq\nAFXIRaRc2XlgJ+8uexdPmoe9OXv55Y5fiI2OZfr10/lT3J+oU6WO2yGWW0Ul18Xl9To7Z3o8TivK\n5s0QFQUffADNmkGNGichUBGRMKaHOkXENV+t/4qrJ15N/HPxDP14KLuzd9O/dX/81g/AZY0vUzIe\nAl6vl6ZNzyQrK6vI8+bMgcsvh6QkuO8+p03lwQednnGAc85RMi4icjIoIReRUuMP+Jm1bha7D+4G\nYPXO1SzyLuLe8+5l8e2LWfrXpTzU6SGiIvTLu1BKSRlFRsY2UlJG/W7d54OZM2HjRuf9r78688Mf\nfhhWrICFC53EPFZdQyIiJ5V6yEUkpKy1LPIuYvyS8UxaNoms37J4/arXGXLOEHx+H5EmUiMKS5HX\n66VJk1ZkZ39JbGwX1q5dRnp6PB4PvPsu7NoFTzwBjzzi9IkboxGFIiLHQz3kIlIm7DywkwvfupBV\nO1cRExlD92bdSW6TTPfm3QGIidQTgKUtJWUUgcCNQDvy8m6lRYso9u51qt49ezpjCi+/3Dk3Qr9D\nFREpFUrIReSk2b5/O5OXTWZfzj4e6vQQtWNrc37987n//Pu5tuW11Iqt5XaIYW3Roq28+WZ18vIe\nBCA3dxiR32LDAAAgAElEQVTW/o+XX76Zm26qSbVqLgcoIhKmlJCLyAnZ79vP9FXT8aR5+GztZ/it\nnwvrX8iIjiMwxpB6TarbIYa1PXvg/fedCSlffXUa8K8CRxOIiNjIihX/olq10W6FKCIS9vQLSREp\nsbxAHvnPn9z32X0kT00mbWsaf7/g7yy5Ywnf3fKdds50UU4O5OU5r599Fm69FdavzyMy8hlg2+/O\n9fmGk5o67pgTV0REJHSUkIuEMa/XS9PmTYuVjFlrmZ85n7s/uZt6z9VjkXcRAPd0uIc5N80h494M\nnu7yNG3i2oQ6bClEIOCMKbztNoiPh08/ddaHDIH58+GKKx4gMjILqHvElQn4/Tf8YeKKiIiUHrWs\niISxlJEpZGzOKHK79F0Hd/HS/JfwpHlYu2stlSIr0eOMHodGE7Y8rWVphixH2LsXnnwSJk50Nu2p\nWhV69YLEROd4/foQFeXl7bfH4fMtK/QeTpW8NY89Npz4+PhSjF5EREBjD0XCltfrpckZTcgekE3s\nxFjSV6UfSsa2/raVrfu30jauLbsP7iZxdCLn1z+fgW0G0rtFb06pfIrL0Ye3TZsgIwM6dXI26WnY\nENq1c7avv/pqJykvaOjQYYwdCz7f80e9Z0zMMAYPNrzyinrJRUROhpKMPVRCLhKmht41lLGLx+Lr\n6iNmVgw3nn0jnW7thCfNwxfpX/DnxD/zw60/APBr9q/UrFzT5YjD2+7dMGWK83DmN99Ao0awbp0z\nIzw7GypXLvy6w3PHlwEJRfwEL7GxrUlPX6YquYjISVCShFw95CJhyOv1kjouFV8HHwC+i3y8WeNN\nbph2A6t2ruLBCx9k7NVjD52vZNxdTz7p9IUPGQJZWfCvf8EXXxzesOdoyTgUnDteVDIO6iUXEXGP\nKuQiYcZaS5/7+jB973QCDQKH1iM2RXBNjWt4/7n3NSHFRX6/83Cmx+Mk3omJMG2aszZwIJx9dsl2\nzkxKasLmzeuLfX5iYmMyM9OPI3IRESlILStFUEIu4WrF9hV40jz87+f/sXHfRrBAwcRuH8S++fte\ncikd1sIvv8D48c7DmVu2QLVqzlb2f/mL29GJiMjxUMuKiAAcmhW+YMsCWr7akqe+e4rAzgCRSyN/\nn4wDVAd/Gz8pI1NKP9AwlZvrfM/IcB7KfPFFOOccmDQJtm5VMi4iEi6UkItUMHtz9vL2z2/T5Z0u\nDPtsGABnJ5zNmO5jWJi8kB0v78Df0F/otb4OPlLHpWqTmBDauRNeew06doQBA5y1xo1h8mSnP3zG\nDOjXD6pUcTdOEREpPUrIRSqIT9Z8Qr8p/Yh7No6bp9/M+l/X0/CUhgBEmAjuaH8Hb7zwBoG2Aah+\nlJuoSh4yH30E11wDCQnw1786U1M6dDh8vG9fqFPHvfhERMQ9SshFyqmADbBwy8JD79/+5W2+Xv81\ng9sN5odbf2DtXWsZdv6wQ8ePnKxyNKqSnxx+P3z1lbODJsDnn8PChXDPPbB4MSxdCvff726MIiJS\nNoTsoU5jTH3gHSAO5/GxN6y1LxpjngF6AD5gHXCztfZXY0wjYAWwKniLedbaO4L3Ogd4G4gFPgbu\nsdZaY0xtYDLQCMgA+lprdxcVlx7qlPJu2bZleNI8TEibwIY9G1h15yqa12nO9v3bqVm5JtGR0YVe\nV3Du+LHEzIph8NmDj7p7pxTOWli0yJmQMmkSeL3w9ddwySWwb5/ThhIZ6XaUIiJSGsrElBVjTAKQ\nYK1dZIypDiwEegJJwFfW2jxjzL8BrLUPBhPyD621rQu514/A3cB8nIT8JWvtJ8aYUcAua+3TxpgR\nQC1r7YNFxaWEXMqrhVsWcuuMW/ll6y9Emki6Nu1Kcptkep3Zi6oxVY95fVLDJDZv3Fzsn5fYIJHM\nDZknEnJYWb4ceveGVasgJgauvNIZU9i9e9FzwkVEpGIqSUIeFaogrLVewBt8vc8YswJItNZ+XuC0\necC1Rd0nmNjXsNbOC75/Byex/wS4BrgkeOo4YDZQZEIuUl78mv0r7y9/n/qn1OfyppdTr3o9YqNj\nebHbi/Rr1Y+4anElup+S65Nr+3ZnLGGtWs7DmY0aOV/33w/XXuusi4iIFEfIEvKCgtXvdjgV7oJu\nwWk5ydfYGLMY2As8aq39FkgECmYSmcE1gLhg4g+QhdMeU9jPHwIMAWjQoMFxfw6RUMvOy+bjNR/j\nSfPw4eoP8fl93HTWTVze9HISqicc2spe3LF/P0yf7rSkfPaZ0yd+3XVOQl6lCnz6qdsRiohIeRTy\nhNwYUw14H7jXWru3wPojQB7gCS55gQbW2p3BnvFpxphWxf05wZ7yQvtvrLVvAG+A07JyfJ9EJPQu\nfOtCFnkXUbdqXf7a/q8kt0mmfb1i/bZLSsjr9dKx46XMnTu7yI2QrD28M+Y118CXX0JSEvz975Cc\nDG3alFLAIiJSYYU0ITfGROMk4x5r7dQC6zcBVwGdbbCJ3VqbA+QEXy80xqwDmgObcfrO8yUF1wC2\nGmMSrLXeYGvLtlB+HpGTacnWJXiWePg8/XPmD55PTGQMj3R6hKrRVencpDNREaXyC6ywlZIyioyM\nbaSkjOKVV0b/7pi18NNPzs6ZH3zg7KJZuzY8+ig89hh06gQRmlElIiInScj+jW+MMcBYYIW1dnSB\n9W7AcOBia+2BAuun4Tyg6TfGNAGaAenW2l3GmL3GmA44LS83AC8HL5sB3Ag8Hfw+PVSfR+Rk8O7z\n8s4v7zA+bTxLty0lKiKKbqd3Y+eBnSRUT6B3i95uhxgWvF4vqanjCAS+JDW1C489Npz4+Hi8Xnjj\nDScRX7sWKlWCq66CPXuchPySS9yOXEREKqJQ1nguBAYBlxljfg5+XQn8B2dbklnBtdeC518ELDHG\n/AxMAe6w1u4KHhsK/BdYizMq8ZPg+tNAV2PMGqBL8L1ImbLr4C52HNgBwOKsxYz4cgQ1KtXglStf\nwXu/l5n9Z5JQPcHlKMNLSsooAoEbgXbk5Q1l+PA3AWenzH/+Exo0gLFjne3rp0xxdtIUEREJlZCN\nPSyrNPZQSsPB3IN8uPpDPGkePl7zMQ9c8ABPdn6SXH8um/ZuokmtJm6HGLa8Xi+NG59LTk4aUBOw\nREZOIDOzM3Fx8WRlObtpioiInIiSjD1UF6TISWStZcjMIcQ/F0/fKX35cfOP3HnunfRr3Q+A6Mho\nJeMu69IlnZyctTjJOIAhImITKSmjMEbJuIiIlD4l5CInwFrLIu8iXv3pVQCMMezN2UvvFr2ZNWgW\nm4ZtYvQVo2kb19blSMOTtfD99/D4485rr9fLqlWLcQY8HZabeyOpqePIyspyJU4REQlvGuMgchzW\n717PhLQJeNI8rNixgkqRlejfuj+1Ymsx6dpJbocX9laudGaFT5gA6enOTpk33QSjRo0iMhL8/iN3\nNk3A77+h0IkrIiIioaYecpES+u+i/3LbzNsA6NSgE8ltkrm25bXUqVLH5cgE4L33oG9fZyxh587O\nrPBevWD/fi9NmrQiO3sZUFhfipfY2Nakpy8rci65iIhIcaiHXOQkOZB7gMlLJ3P1xKuZsWoGAJ0b\nd2bkZSPJuCeDb27+htvb365k3CV798K4cXD55fBacF5Tly4wejRkZsLnn8ONN0KNGgUnqxytSfxw\nlVxERKQ0qWVF5AgBG+DL9C8ZnzaeqSum8pvvNxKrJ7I3x9lotnGtxjzU6SGXowxf1sLMmU5LyowZ\nkJ0NTZpATIxzvFYtGDbs99fkzx33+ZYVeW+fbzipqa0PzSUXEREpDUrIRXAezsz6LYuE6gkEbIBB\nHwwiOy+bfq36MbDtQC5qeBERRr9QcksgAOvWQbNmzjb2jz4KXi/ceqvTktKhw+Ht7Qtz7Op4PvWS\ni4hI6VMPuYS1dbvW4Unz4EnzcCD3ABvu3UCEiSBtaxrN6jSjclRlt0MMa8uWHX44c+dOZ+OeqlVh\n/XpISoLo6OLdJympCZs3ry/2z01MbExmZvpxRi0iIlKyHnJVyCUsfbT6I1K+SWH+5vkAXNzwYpLb\nJJMXyCMmMoY2cW1cjjC8ff45DB8Ov/ziPJzZtSsMHAiRkc7xku6cqeRaRETKMiXkEhb2+/YzbeU0\nLm50MUk1ktjn28fBvIP8u8u/6d+6P/VPqe92iGHt11/h/fed1pNWrZwxhZUqwYsvQr9+EBfndoQi\nIiKho5YVqbBy/bnMSp+FJ83DtJXTOJB7gBeueIF7OtyDtRZTVNOxhFxODnz8MYwfDx995Lz/xz+c\nTXxERETKO7WsSNjbm7OXZi83Y9v+bdSqXIuBbQaS3DaZjg06AigZd1lurtN24vVC3bpw++3Ow5l/\n/rPbkYmIiJQ+JeRSIazeuRrPEg+7s3fz0l9eokalGgw5ewjt67Wn2+ndqBRVye0Qw1pamlMJX7vW\naU2JjoaHH3ampnTuDFH6m0hERMKY/jUo5VbWb1lMXjoZT5qHn7b8hMHQ7fRuBGyACBNBymUpbocY\n1jIznQkpHo+TkEdFwRVXOHPDK1eGO+90O0IREZGyQYOVxXVer5emzZuSlZV1zHP35ezDH/AD8Oz3\nz3LvZ/eSF8jjucufI/O+TD5O/ljzwkvA6/XStOmZxfqzL47du+HgQef15MkwYgRUrw6vvOK0p3z4\noZOMi4iIyGHKXMR1KSNTyNicQcrIwivauf5cPlz9IddPuZ64Z+P4OuNrAO457x6WDV3GotsXcd/5\n91Gver3SDLtCSEkZRUbGthPaLj47G6ZMgV69nGkoU6Y46zff7GzmM3cuDB0Kp556koIWERGpYDRl\nRVzl9XppckYTsgdkEzsxlvRV6Ye2LN95YCePff0Y7y57l50Hd1Intg59W/Xl7vPu5sxTz3Q58vLP\n6/XSpEkrsrO/JDa2C+npy0q0Xfy+fXDPPU5P+N69EB8P/fvDbbdBixYhDFxERKQcKMmUFVXIxVUp\nI1MItA1AAvjb+Bn272HMzpgNQNWYqkxbOY0uTbows/9Mtty/hVe7v6pk/CQ5vJ18u0PbxRfFWli8\nGKZOdd5XqwaLFkHv3jBrltMzPnq0knEREZGSUoVcXHOoOn57NlQD/EAkNK/VnFV3rwKcdpXoyGLu\njy7Fdrg6vgxIALzExrYutEq+fr2zdb3HAytWQEKCk3xHRDhJuiZIioiI/JEq5FIupIxMwdfH5yTj\nAJEQuTKSC9ZdcOgcJeOhcbg6nhBcSSi0Sv6Pf0CTJvDoo1CnDrz2mjMxJSL4N4eScRERkROnhFxK\njc/vY/rK6fSb0o+09DRSx6USiAv87hx/op/Jb08+aVM/5I+8Xi+pqePw+Yb/bt3nG86bb+7hiiuy\nWbHCWevcGUaOdKrk337rbOBTp44LQYuIiFRgmkMuIRWwAeZunMv4JeN5b/l77M7ezWlVTiNnbo7T\nOx57xAXVnV7ylJEpvPLSK67EXNH9sTqeL4Hc3LHMnfsrGzZUpkULuOgi50tERERCRz3kEhLZedlU\njqrMsm3LaD2mNVWiq9DrzF4kt0mmVZVWnNHiDLKHZEP1Qi7eB7Fv/n7iipwcXq+Xxo1bkZOzHCjs\nz3YnlSufyfr1afqzFxEROQHqIRdXbNqziVFzR/Gn1/7EbTNvA6BV3VZM6zeNrX/fyvje4/lLs7/w\n9NNPO9XxwpJx+F2VXE6edevgmmt+JidnJYUn4wB1CAQGntBcchERESkZVcjlhE1Mm8gbi95gTsYc\nLJbzEs9j8NmDGXz24D+ce2iyytGq4/lUJT9pJk2CF1+EefPyV3KASkVccfSJKyIiIlI8qpBLiZRk\n63pw2lE+XP0h+f8xNztjNlv2beHxSx5nzV1rmDd4XqHJOBSYO15UMg6qkp+A/fth4kTIyXHer1rl\nrHXoMJPo6H9SdDIOR5u4IiIiIqGhCrkw9K6hvP7W69xx6x1HfZAyYAN8s+Ebxi8Zz5TlU9iTs4d5\nt87jvKTzOJB7gNioWEwxZuAlNUxi88bNxY4tsUEimRsyi31+uMrLgy++gPHjYdo0JwGfMQN69HCO\nRUVBUlITNm9eX+x7JiY2JjMzPYRRi4iIVFwlqZArIQ9zRW1dn2/hloX0nNyTzL2ZVIupRu8WvUlu\nk8xljS8jKkKDety2apUzCWXbNqhVC667DpKToWPHw/PCRUREpHSVJCFXNhXmjty6PmVkCsP/NZyJ\nSyfSqGYjrm99Pc3qNKN9vfY80/UZrj7jaqpEV3E77LC2Zo2za2bNmnDvvdC0KXTvDtdcA926QaVj\ndaSIiIhImaIKeRj7wwOW2RCxI4JAkrNZz5Czh/B6j9fdDVIA2LrVeTjT44GffnJ2yBw0CMaNczsy\nERERKYwq5FIsf3jAsjIEagY498C5TBoxica1GrsaX7g7eBBigxsnDR4MH34I7drBs8/C9ddDYqK7\n8YmIiMjJoQ7TMOX1ekkdl4qvg+/3ByykvZZGbM6RW2hKacjNdRLv/v3htNNg40Zn/YknYNkyWLQI\n7r9fybiIiEhFooQ8TB11/KDGDbpiwwYYOhQSEpzJKLNmwQ03HD7+pz9By5buxSciIiKho4Q8DB21\nOh7k6+AjdVxqseeSy/FZsQLS0pzXfr/TD96lizOucMsWePVVaNDA3RhFREQk9JSQh6Fjbs6jKnnI\nbNkCo0fDOec4Fe//+z9nvUkT2L7deXCzRw+IiXE3ThERESk9SsjDzLGq4/lUJT/5brgB6td3esAj\nIuD552HMmMPHq2iapIiISFhSQh5mtHV96fD5YPp0uO02px0FnD7wRx+FlSud0YX33gtH7MEkIiIi\nYUhzyMOMtq4PnUAA5s51ZoW/9x7s2gWnngrffw/NmrkdnYiIiJQmzSGXo1JyffL5/RAZCTNnQs+e\nTutJz57O9vVdu0J0tNsRioiISFmmlhUJa16vl6ZNzyxxr3xmJjzzjNOG8vTTztoVV8D//ufsqunx\nwJVXKhkXERGRY1NCLmEtJWUUGRnbSEkZdcxzrYW33oJLL3XGEQ4f7uyk2bSpc7xyZRg4EKpVC3HQ\nIiIiUqEoIZew5fV6SU0dRyDwJamp4wqtkufkOD3gAMY4s8K3bIHHH4c1a2DePGcbexEREZHjpYRc\nwlZKyigCgRuBdvj9NxyqkgcCMHu2MyElPh4uvhh27HCumTbNmZLyf/8Hp5/uWugiIiJSgeihTglL\n+dVxn28ZAD7fcFJTW3PBBY8yYkRtMjOd1pNevZyHM2vWdK6rVcvFoEVERKRCUkIuYelwdTwhuJKA\n338DM2eO5ayzHuCZZ+Dqq7VZj4iIiISeEnIJO8uXZ/Hmm5CX9/Tv1n2+4cyY0Zr09EHEa8ceERER\nKSXqIZewkL//ld8P7dtXIS/veaDSEWcl/K6XXERERKQ0hCwhN8bUN8Z8bYxZboxZZoy5J7he2xgz\nyxizJvi9VnDdGGNeMsasNcYsMcacXeBeNwbPX2OMubHA+jnGmLTgNS8ZY0yoPo+UP34/fPEF3Hwz\nnHuuk5Rv2+YlL+8eYHuh1zi95IVPXBEREREJhVBWyPOA+621LYEOwN+MMS2BEcCX1tpmwJfB9wB/\nAZoFv4YAY8BJ4IF/AOcB5wL/yE/ig+fcVuC6biH8PFJOrFwJ998P9es7O2W+/z60bg379zu948bU\nBE47ytWqkouIiEjpCllCbq31WmsXBV/vA1YAicA1wLjgaeOAnsHX1wDvWMc8oKYxJgG4Aphlrd1l\nrd0NzAK6BY/VsNbOs9Za4J0C95Jy5Hh3yyxo/Xr49Vfn9Q8/wMsvO1Xxd991ds5MTYV9+/Inqwwv\n8l6qkouIiEhpKpUecmNMI6AdMB+Is9Z6g4eygLjg60RgU4HLMoNrRa1nFrIu5UxJdsssaMcOGDMG\nOnaEJk3gnXec9X79ICvLmRl+3XXObpr5P+f3k1WORlVyERERKT0hT8iNMdWA94F7rbV7Cx4LVrZt\nKcQwxBizwBizYPv2wnuHxR3F2S3zSAcPQo8ekJAAQ4c6lfGnnnJmhoMzqrB27T9eN2PGdHy+FwBz\nzC+f7wWmT592cj6kiIiISBFCmpAbY6JxknGPtXZqcHlrsN2E4PdtwfXNQP0ClycF14paTypk/Q+s\ntW9Ya9tba9ufdtrReofFDUfbLbOgvDz4/HOnGg5OxdsYGDYMfv4Z0tJgxAinZ7womZnpWGuL/ZWZ\nmX7yP7CIiIjIEYy1oSlQByeejAN2WWvvLbD+DLDTWvu0MWYEUNtaO9wY0x24E7gS5wHOl6y15wYf\n6lwI5E9dWQScY63dZYz5EbgbpxXmY+Bla+3HRcXVvn17u2DBgpP7YeW4eL1emjRpRXb2Mpw2Ei+x\nsa1JT19GXFw8CxaAxwOTJjl94AkJsHEjRGl6voiIiJRxxpiF1tr2xTk3lKnNhcAgIM0Y83Nw7WHg\naeBdY8ytwAagb/DYxzjJ+FrgAHAzQDDxTgF+Cp73L2vtruDrocDbQCzwSfBLyomj7ZaZkjKK2rVH\n88QTEBMD3bs729d3765kXERERCqeYlXIjTGR1lp/KcQTcqqQlw1/rI7n20Fs7Bl8+OEq0tNPpU8f\nqFXraHcRERERKZtKUiEvbg/5GmPMM8E54iIn7B//GE1u7ov8ceLJqfj9N/D++yMZPFjJuIiIiBy/\n3Nxcxo0bR25urtuhFKm4CfmfgNXAf40x84JTS2qEMC6pgPLynB5wr9fLO+9Mxu8fWOh5mgMuIiIi\nJyo3N5d+PXpw3+DB9OvRo0wn5cVKyK21+6y1b1prLwAexNk502uMGWeMOT2kEUq5Zi3Mmwd33QX1\n6jmjCVNSRmFtH5wRg4XRHHARERE5fvnJeO6335KRl0fut9+W6aS8WAm5MSbSGHO1MeYD4AXgOaAJ\nMBPnYUyRP3jrLWjWDM4/H/77X7jkErjzzl289ZZ2yxQREZHQKJiMTzlwgOrAlAMHynRSXuwecpyt\n7Z+x1raz1o621m611k4BPg1deFKeZGXBCy/Anj3O+wMHoFEjJzHPynK2sf/ppxSs1W6ZIiISPspL\nH3NFcGQyXim4XomynZQfc8qKMSYSeMRa+6/SCSm0NGXl5Nq3Dz74AMaPhy+/hEAA3n8fevd22lXM\nEV0pSUlN2Lx5fbHvn5jYWBv0iIhIuZWbm0uP3j348usv6XxpZ2ZOnUl0dLTbYVVIR0vGC8oBrq1S\nhehOnZg8M7T/LE7qlJXguMNLTzgqqXDS0yEuDm68EdasgYceguXLnWQc/piMg3bLFBGR8JGfjH+7\n/lvy7s7j2/Xf0qN32avOVgTFScah7FbKi9uy8r0x5j/GmE7GmLPzv0IamZQp1sLcuTB0qLNNPUDj\nxnDffc56ejo88QS0aOFunCIiImVBwWT8QK8DUAkO9DqgpDwEipuM5yuLSXlxNwb6upBla6297OSH\nFFpqWSmZlSuddpQJE2D9eoiNdSriY8a4HZmIiEjZ9IdkvOAu03lQ5YMqdGrcSe0rJ0F+Ml7lm29I\nPXiQkvxphrp9pSQtK8VKyCsSJeTHtnWr04oCMGiQk4x37epsX9+zJ1Sv7m58IiIiZVWRyXg+JeUn\nx/79fHPffeS8+SZdrD3qMOWi7AMaRUUx+r//5cYbbzyp4YUkITfGdAdaAZXz18rjg55KyAu3Zw9M\nnQoeD3z1FfzyC7Rpc7gqHh/vdoQiIiJlW34yPmfnHLK7ZhfdGKyk/Pjk5cGsWU7C8sEHcOAA2ytX\nZnZeHtfk5RFTgluVpQp5ceeQvwb0A+7C2c3lOqDhcUcoZUZ6Olx3nVMRv+UWyMiAxx6DOnWc440b\nKxkXERE5lvSd6bQe2prPT/+c7CuOkYwDRKmnvNishfnzD+8yeOWV8PHHMHAgzJlDzV9/ZWLnzvSp\nUoWcYt6yNKetFEdxe8iXWGvbFvheDZhqrb089CGeXOFeIQ8E4LvvnAkonTrB9u1w1lnOZJSBA+Hc\ncwufjiIiIlJe5ebmMmHCBAYMGHBSE69fs39lyvIpjP9lPHM2zHFKlpajb0RdGFXKj271aqcS7vHA\nunVQqRL06OEkLN26Oe+DSvJgZ2kl4yWpkBfW2VSYg8HvB4wx9YCdQOPjCU7ckZbm/O954kTYuNHp\nCf/8czjtNNi0CSKKO29HRESkHCk4B3zilIknnPRm52Xz0eqP8KR5+GjNR/j8PuKj4omYG0HgzwGO\nOeLjSFFw4OoDfPnSl0yYMOGk9zGXO1lZMHmyM1FiwQKnSnjZZfDII0718JRTCr0sOjqayTNn0q9H\nD64tI3PIS6K4adiHxpiawDPw/+zdeXzU5bn//9cnyYQk7DtIWAKyMxE3VJA1iKAEBMKS+USxLVVL\nreerXazLaT3ld3ostdp6jsfag1rafBL2VUDFCBhF0bpl2HdkCfsiMFlmuX9/3AkJkEwy2SaZuZ6P\nhw9hMss9hGHeuee6r4uvgIPAgtpalKhZKSmQmAgvvQT9++u/48uWlXxdwrgQQohQVFN9wH3Kx4YD\nG5i1ahYdXupAyuIUNh/ezE9u+wmfz/qcQ788xD0x9xC3Kg48AS7SA3Gr4kgamYTD4QjwxiHi4kX4\nxz/g3nuhUyf4f/8PvF4dXA4fhg8+gB/8oNwwXqw4lNuGDiWljPKV+hrGoQpdVgzDaATEKKUu1M6S\naleol6ycPw9LluhzDkuXQkwM/POf+tDmtGnQrl2wVyiEEELUvjK7nQRQHqKU4tsT32LlWGRuzeTo\nxaM0iW7C5L6TMe0moxJGERUR5f/xKhLO5SpuN7z3nv74fuVKyMuDbt10SzfTrNZgk7LKV4IRxmus\ny4phGJP93Vgptczf1+ujUAzk+fn6bEN6OqxZA4WF0KuX/vvdp0+wVyeEEELUrer0AT90/hAZzgws\np8W2U9uIiohi7I1jMe0mE3pPIM4WV7XHvVY4hnGlYPNmHcIXLYIzZ3QXiWnTdAgfPLjGDrKVDuUZ\nLheOIOyM12Qgf9vPbZVS6oeBLi7YQiWQ+3zgckGTJpCVBaNH624oM2bov9O33iqHM4UQQoSfqvQB\nv4+MZ/MAACAASURBVOi5yOJti0l3pvPxdx8DMKTzEEy7ydT+U2kT16ZWHz/kw/iOHTqEl54yOGGC\nPpw5ZgxEB9KssPKKQ/mmrCyGJyXVeZmKDAbyo74G8tzcXO6+eySffLKRDuX0GVQKcnJK/k7PmKHL\nq7xe2LABRoyAqMoe0xVCCCFCTKA71NFfRNO8T3POtz6P2+emb5u+mHYTh91BQsuq966QSZ3AsWO6\nk4Rlwddf6wNro0frXcNJk+psymBtddipDBkM5Ed9DeSzZz/JG2/M57HHHua1116+7usvvwxvvQXb\ntunQPXYsPPoojB8fhMUKIYQQ9UyVargBI8+gy4UuLHp2EbfH345RgyUT1alhb5CunTKoFNx2m94J\nnz497Aab1HggLxoMFAeMBOYBKcDnSqkfVWehwVAfA3lubi7du/cnPz+L2NjR7N+/jejoDmzcqDv8\ngP5h8uRJ/Xd66lRoU/lPz4QQQoiQVtUwDtRqSL5qXRNcxK0KwTBeWAjr1ukQvmoVFBRAjx56J9zh\ngN69g73CoKmNQC6DgWrR7NlP8uabUFj4CpGR8+nc+VaOHh2A260nZ3btqv++11KJlRBCCNGgzZ8/\nn1k/nYXnCU/gfcABCiDq1SjmvTavxvuAl+6DnjQyKTTCuM8Hn3yiu0ksXgznzunBJtOny5TBUmoj\nkG9RSt1hGMZnwGTgLOBUSvWs3lLrXn0L5CW743uBVsWX8thjzXjkkcYMHCh/p4UQQgh/ikPvR0c/\nIm98HkQGcOM6KCMJZh1zjdq6teQg23ffQVyc/gjfNHV9eEN+brWgNiZ1Fg8Gmgt8WXTZvKosTlxt\nzpy5+HwzKQnjYLP9kYgIuPnm62vJhRBCCFHC5XaxcudKImdEkr87X488rOz4+jqq6bbZbA13AueR\nI/pwZnq67iwRGak7o/z+9zBxom73JqqtoraHtwOHlVLHi37/EJAG7AReUEqdrZNV1qD6tENesju+\nDehY+ivExg5g//5t5XZcEUIIIcKVx+cha38WltNi2Y5lXHZfJr5ZPNP7TefTNz/lm+3fSB/w6iie\nMmhZsGmTPpx5xx16J3z6dJkyWEk1uUP+BjC66E6HAS8CPwMGAn9DH+4UVVSyO97xmq90xOt9iDlz\n5pbZcUUIIYQIN0opvjj2BVaOxcJtCzlx+QTNGzUndUAqZqLJsK7DiDAicI8qOki5XPqAB6R4yqBl\nwTvvlEwZfOEFfTjzxhuDvcKQVtEO+bdKqZuKfv0acEop9ULR779RSg2sk1XWoPqyQ17+7viVa8gu\nuRBCiLC39+xerBwLy2mx5+weoiOjGd9rPKbd5L6e9xETFXPdbaQPeCX5fHoH3LL0jviFC9C+fcmU\nwdtuk4Ns1VCTO+SRhmFEKaU8QBLwSAC3FX6UvzteTHbJhRBChKcTl06wcNtCLKfF50c/x8BgRLcR\nPD3kaab0m0KLmBZ+b2+z2Vi9bPX1O+USxq+eMpiZqWvEmzTRfZZNE0aNkimDQVDRDvlzwH3AaaAL\ncItSShmGcSMwXyk1pG6WWXPqww55xbvjV64pu+RCCCHCwqXCS6zYuQLLabF+33q8ystN7W/CtJuk\n2lOJbxYf8H2GRR/wyjp0SHdHsayrpwyaph5jHxcX7BWGnBpte2gYxp3o1Pi+Uupy0WW9gCZKqa+q\nu9i6Vh8Ceem+4xWJjn6SWbMM2SUXQggRctxeN+v3r8dyWqzYuQKX20WX5l1wDHBgJpoMaDeg+o8R\nin3AK+vsWd0n3LIgO1tfNmSIDuEyZbDW1Xgf8lBSHwJ5fHx3jh49UOnrd+qUwJEj+2txRUIIIUTd\nUEqx5egW0nPSWbhtIaddp2kZ05Jp/adh2k2GdBlChBFRo48ZMn3AKyMvTx/KtCx9SNPthr59SyZn\nJiQEe4VhQwK5H/UhkAshhBDhZtfpXVhOiwxnBvvO7SMmKobkXsmYdpNxPccRHSnjqKvM64UNG3QI\nX7oULl6Ejh0hNVVPzpQpg0FRG4OBhBBCCNGABWOX+Pil4yzYuoD0nHS+zP0SA4Ok7kk8P+x5Jved\nTLNGzepkHSFJKfj665LDmbm50KwZpKTo3fARI/QQH9EgSCAXQgghQlzpOurMJZm1Wkf9fcH3LN+x\nHMtpkXUgC5/ycUvHW/jTmD8xY8AMbmh6Q608btg4cECHcMuCnTv1uPr77tMhfPx4iI0N9gpFFUgg\nF0IIIUJY6U4jnic8ZK/KJnlyco2G8kJvIe/tfY90Zzqrdq0i35NPQosEnr37WRx2B33b9q2Rxwlb\np0/DokU6hG/erC8bNgyefFLviLdqFdz1iWqTQC6EEEKEqLIG5LgmucheXv1Q7lM+Nh/ejJVjsWj7\nIs7mnaV1bGt+OPCHmIkmd8XfhSF1y1XncsGqVTqEv/sueDwwYAD813/p2vCuXYO9QlGDJJALIYQQ\nIajcaZXVDOXbT22/Mjnz0IVDxEbFMrHPRNLsaYzpMQZbZIh3MalNHg9kZekQvnw5XLoE8fHw1FO6\nJCUxMdgrFLVEArkQQggRYvyOjoeAQ/nR74+SuTUTy2nxzfFviDAiuKf7PcwZOYcH+jxA00ZNa/cJ\nhTKl4F//0iF8wQI4cQKaNy8ZXz9sGETUbBtIUf9IIBdCCCFCSIVhvFgFofxC/gWW7liK5bTYcGAD\nCsXtN9zOn+/9M9MHTKdDE5kgXS179+oQnpEBu3dDdLQ+lJmWpg9pNmoU7BWKOiR9yIUQQogQUekw\nXpoH4pbrkfJLFi3hg0MfkJ6Tzju736HAW8CNrW7EtJs47A56te5V688hpJ08CQsX6iC+ZYvuDT5i\nhN4JnzIFWrQI9gpFDZLBQH5IIBdCCBGKqhTGi3kg8rtIjC4GnigPbePaMmPADEy7yaBOg+RwZnVc\nugQrV0J6Oqxfr4f43HSTDuGpqbpGXIQkGQwkhBBChJmMjAyyNmThecIT+Lt7FHgTvBg7DJ4a8RR/\nePQPREVIRKgyt1uHb8uCFSt0x5QuXeBXv9JBvH//YK9Q1DNySkAIIYQIAQ6Hg6SRScStigNPgDf2\nQuzyWMa4xvDirBdDLoy73W7mz5+P2+2uvQdRCj77DH72M+jUCe6/H9atgwcfhI8+0gN9fv97CeOi\nTBLIhRBCiBBgs9lYvWw1QxOGErc8gFDugbhlcQzrMqxWJ3gGi9vtZnpyMk/NmsX05OSaD+W7dsFv\nfgM9e8Jdd8G8eboufOVKOH4c/vpXGDpUOqUIv+RvhxBCCBEiikP54O6Dif48GnwV3KDUgc5QDuPu\n7GwOejy4s7NrJpQfPw5//jPcfjv06QP/+Z+QkABvv63bFi5aBBMm6M4pQlRCaH0mJYQQQoQpr8/L\npkObsHIsvhj8BYUFhZAHNKLs7bcwCuNLXC4aAUtcLlKKQvnC1QE+54sX9bCe9HQ9vMfng1tugT/9\nSfcMv+GGWnsuIvRJIBdCCCEaKKUU3574FivHInNrJkcvHqVJdBMm953MjH4zeOXnr/DJgU+u77oS\nhmEcCDyUFxbCe+/pw5mrVkFent4Jf/ZZfTizT5+6eDoiDEjbQyFEWMrNzWXk3Xez8ZNP6NBBBpyI\nhuXg+YNkODOwnBbbT20nKiKKcTeOw7SbJPdOJs4WB5TTCjFMw3hpBUBKXBy2oUOvD+VKwebNOoQv\nWgRnzkDr1jB9ug7hd92l+4cLUYF60YfcMIy3gPHASaXUgKLLFgK9i67SAjivlBpoGEY3YAewq+hr\nnymlHiu6za3A34FYYC3wb0opZRhGK2Ah0A04CExTSp2raF0SyIUQAE/Ons38N97g4cce4+XXXgv2\ncoSo0BnXGRZvX4zltPj4u48BGNJ5CKbdZGr/qbSJa1Pm7a4K5RNcxK0K7zBe7LpQvmdPyeTMgwch\nNhYeeECH8DFjIMT+rETtqy+BfBhwCfhHcSC/5ut/Ai4opX5XFMjfKed6nwNPAFvQgfxVpdQ6wzDm\nAmeVUi8ahvFroKVS6umK1iWBXAiRm5tL/+7dycrPZ3RsLNv275ddclEv5bnzWL17Nek56by7913c\nPjf92va7MjmzW4tulbqf4lCetSGLpJFJYR/GixUA6TYbI2Ni6H7xou6Ecs89OoQ/8AA0bVrbyxYh\nrF4MBlJKfVQUtK9j6JFf04BR/u7DMIyOQDOl1GdFv/8H8ACwDpgIjCi66nxgI1BhIBdCiLlz5jDT\n5+Nm4CGvl7lz5sguuag3vD4vHx74EMtpsWzHMi4WXuSGpjfwxB1PkJaYxk3tbwp4cmZx95WMjAwc\nDoeE8SKNgB+53ez2epnXpw8z16/HJpMzRRAE61DnUOCEUmpPqcsSDMP4GvgeeF4plQ10Ao6Uus6R\nossA2iulcot+fRxoX96DGYbxCPAIQJcuXWrmGQghGqTc3Fzmv/UW2wsLAfhVYSED3n6bX/37v8su\nuQgapRRf5X6F5dSHM49fOk6zRs2Y2m8qZqLJ8K7DiYyIrNZj2Gw2Zs6cWUMrrl8yMjLYlJXFQY+n\n0mG8tI4+H0/v3YstKytk/4xE/RasQJ4KZJb6fS7QRSl1pqhmfIVhGJUeZVVUU15u7Y1S6m/A30CX\nrFRxzUKIELAlJYXthYUUR++OyC65CJ795/Zj5VhYTotdZ3Zhi7Bxf6/7Me0m43uNJyYqJthLbBAc\nDgcrMzNxBLhDDrpsxREXx/ChQ3E4HLW1RCH8qvPBQIZhRAGT0QcyAVBKFSilzhT9+ktgH9ALOAqU\n/uwovugygBNFJS3FpS0na3/1QogG58wZWLwY0LvjEZ99Rqtrzs78qrCQ+W+/zfHjx4OxQhFmTl0+\nxWufv8bgNwfT49Ue/Gbjb2jfpD1/G/83TvziBMunLyelX4qE8QDYbDZ9MHPoUJ6Kian0kFK/3VaE\nqEPB2CEfDexUSl0pRTEMoy36gKbXMIzuQE9gv1LqrGEY3xuGcSf6UOdDwH8X3WwVMBN4sej/K+vy\nSQgh6jGXC1av1gM83n0XPB44cIC5c+cSERnJBN/V4wtll1zUtsuFl1m5ayWW0+L9fe/j8Xmwt7Pz\nYtKLpNpT6dJcyimr5fBhbJmZLD12DCM/Hy/gBfwV+UgYF/VJbXZZyUQfumwDnAB+q5R60zCMv6Pb\nGv611HWnAL8D3OhBv79VSq0u+tptlLQ9XAf8rKhEpTWwCOgCHEK3PTxb0bqky4oQIW7tWt0v+NIl\n6NQJUlPBNMlt147+PXqwLT+fjmXcLBcYIB1XRA3y+Dx8sP8DLKfF8h3Luey+THyzeBwDHJiJJont\nE4O9xIbt/HlYskT/4P3RR7p/+J134k1N5UcrVnBmy5aq9SEXoobUi7aH9ZUEciFCiFLw5Ze6d/CI\nETBxIhw5Ar/9LaSlwbBhEKn3yJ6cPRvefJNXig5zluXJ6GiMWbNklzyEud3uWu00opTii2NfkJ6T\nzsJtCzl5+SQtYlrow5l2k6FdhxJh1Hm1aOjIz4c1a/Rrfs0aPUmzd2/dptDhgB49AP9dVySMi7oi\ngdwPCeRC1A/VmpS5b59+Q7Ys2L0boqPhN7+B554r97H6d+9e7u74leshu+ShrDZ7ce85swfLqQ9n\n7j27l0aRjRjfazxpiWmMu3EcjaKq0vtDAODzwaZNeid86VK4cAE6dLjy6Re33FLm5MyyQrmEcVGX\nJJD7IYFciPoh4EmZ+fkQE6N3xRMS4NAhvStumjBlCrRs6fexKtodv3Jd2SUPSbUxrfLEpRMs3LaQ\n9Jx0vjj2BQYGIxNGYtpNJvedTIuYFjX8LMKIUvDtt/qH7sxMOHpUD+mZPFm/5keNuvLplz+lQ3mG\ny4VDwrioQxLI/ZBALkTwVXpS5uXLsHKlflP+5hs9ztpm0/WiCQnQuXOlHq97fDwHjh6t+IpFEjp1\nYv+RIxVfUTQIV4XxSS7dzsADccsDD+WXCi+xfMdyLKfFB/s/wKu8DOwwENNukjoglU7NOlV8J6J8\nhw7p0fWWBdu2QVQUjBunQ3hyMsTFBXyXxaF8U1YWw5OSJIyLOiOB3A8J5EIEX+kd6zJ3pL/6Cl5+\nGVas0KG8SxddH/rsszLKWgSkzDBerJKh3O118/6+97GcFit2riDPk0fX5l0x7SZmokm/tv3q5smE\nquLWpJYFH3+sL7v7bh3Cp06F1q2r/RC1fXZAiLJIIPdDArkQwXVtPXcuMCAmht1Ll9I6MRHi42HV\nKnj4Yf1mbJr6zTlCDsKJwPgN48XKCeVKKT498ilWjsWi7Ys47TpNq9hWTOs3DTPRZHDnwXI4szry\n8nRrUsuCdevA7YZ+/UoOZ3brFuwVClFtEsj9kEAuRHCVVc99CmgL+mDmf/yH7hvu9UIjOQgnqqZS\nYbxYqVD+x7/9kUU7FpGxNYP95/YTExXDhN4TMO0mY28cS3RkdJ09h5Dj9cKGDfpw5rJlcPEi3HCD\nDuCmCTfdVObhTCEaKgnkfkggFyJ4yut2UgD8wmbj+a1bad+rV7CWJ0JEQGG8mBeM7w1US0WEEUFS\nQhKm3WRS30k0a9Ss1tccspTSJWiWBQsWQG4uNGsGKSk6hA8fXqnDmUI0RIEE8mBM6hRChJuLF2H5\ncrb+5S/M9Pmuaz3YCIgyDP7wl79IdxNRLVUK4wCRoFoobN/YGGIMYc0za6TWuDr279eHM9PTYdcu\n3Zr0vvt0CB8/XndMEkJcITvkQoja4XbDe+/pnbGVKyEvj/2GQaxSMilT1Jr58+cz66ez8DzhocwR\njRUpgKhXo5j32jxmzpxZ4+sLaadOwaJF+jX/6af6suHDdQhPSfHbmlSIUBTIDrmcSBFC1Byl9BAP\ngD/8QbcpW78eHn6YVx94gP+Oiip3ME9H4CGvl7lz5tTVakUIcjgcJI1MIm5VHHgCvLEH4lbFkTQy\nCYfDUSvrCzmXL+s+4ePH63rwxx+HS5fgxRd1C8ONG+HHP5YwLkQFZIdcCFF9O3boXbGMDPjzn2HC\nBP1m7HTCmDHknjkjkzJFnXG73Yx0jGRL5BY8fTxQmXOCVexLHpY8HsjK0uUoy5frUB4fr3fCTRPs\n9mCvUIh6QWrIhRC1Ly8PXn9dB/GvvtJtCUePhubN9de7dtX/AXPnzCmzdvxapXfJpZZcBOrI90fI\ndGZiOS2+HfAthjKIOB6Br50P/J0blDBeMaXgiy9KDmeePAktWpR0SBk6VFqTClENskMuhKi877+H\nvXvhllt0jXinTjp0mybMmAHl7GrLpExRW87nn2fp9qVYTouNBzeiUAzqNEiPr+81mVkPzapSH3JR\nZO9eHcItC/bs0a1Ix4+HtDQ9QVNakwpRLml76IcEciECVFioB3dYlh7k0b697qAQEaEn7NXAFD0h\nAlHgKWDtnrWkO9NZs3sNBd4CerbqiWk3cdgd9Gzd88p1a2JSZ9g5cQIWLtSv+c8/173BR47UP3hP\nnqx3xoUQFZKSFSFEzfjf/4Xnn4dz56BtW5g1S78pFw/vkDAu6ohP+fjo0EdYORZLdizhfP552jVu\nx6O3PoqZaHL7DbdjlDFUxmazsXrZah3Kl5cK5RLGr3bpEqxYoUP4+vV6iM/AgfDHP0Jqqv40TAhR\naySQCyFKbN2q35Aff1y/Abdrpz+WNk245x4I99Ai6lzOiRysHIvMrZkc/v4wjW2Nmdx3MqbdJKl7\nElERFb+NXRfKJ7iIWyVhHLcb3n+/pDWpy6VL0J5+Wr/m+/UL9gqFCBtSsiJEuDtyRLctsyz49ls9\nNS8zE6ZODfbKRJj67sJ3ZDgzsJwWW09uJSoiint73ItpN5nQewKNoxtX6X6Ly1eyNmSRNDIpPMO4\nUvDZZ/r1vnAhnD4NrVrBtGk6hA8eLIczhaghUrIihPBPKV12cuyY3hHz+eCOO+DVV2H6dL0zLkQd\nOpt3liXbl2A5LT469BEAd8XfxWv3vcbUflNp27httR+jeKc8IyMDh8MRXmF8586S1qT79+tJmRMm\n6BA+dqyepCmECBrZIRciXBQUwJo1+k05Jkb/H+Bvf4NRo+DGG4O7PhF28j35vLP7HdJz0lm7Zy1u\nn5s+bfpcOZzZvWX3YC+xYcvN1S0KLQu+/FLvfCcl6RA+aRI0axbsFQoR0mSHXAhR4tNP4a23YPFi\nuHBBd0l5+OGSrz/ySNCWJsKP1+dl48GNWE6LpTuW8n3B93Rs0pHHBz1OWmIaN3e4uczDmaHC7XbX\n7g7999/rYT3p6fDhh/rTr1tvhZdf1q1JO1Y0DUAIEQwSyIUINUrpCZn9++t68EWLdE345Mm6d/Co\nURAlL/1wVuuh8BpKKb45/g2WUx/OPHbxGE2jmzKl3xRMu8nIbiOJjPA3uSc0uN1upicnsykri5WZ\nmSxcXUM17IWF8O67eid81SrIz4fu3eG55/Tgnj59qv8YQohaJSUrQoSK777T9aHp6bBtm94dGzlS\nH9qKjYXGVTsIJ0JLXR5sPHDuwJXDmTtO78AWYWNcz3GYdpPkXsnE2mJr5XHro+Iw7s7OJsPlwhEX\nh23o0KqHcp8PNm/WIXzRIjh7Ftq00WdATBPuvLOkPakQIihkMJAfEshFyNm/X5egZGfr3w8eXDI5\ns1WroC5N1C9XDcmppdZ/p12nWbxtMZbT4pPDnwBwd5e7SbOnkdIvhdZx4de7vnQYX+Jy0QgoAFKq\nEsq3bSs5nHnokP5h+4EH9Kdf0ppUiHpFArkfEshFfZKbm8vIu+9m4yef0KGcsfPXycuDd97R5SiT\nJ+vewSNG6I4JDof+qFqIa5Q5sbKGhuO43C5W7VqF5bR4d++7eHwe+rftj2k3SbWn0q1Ftxp9Lg1J\nWWG8WKVD+dGjJa1Jv/lGv/bvuUf/4P3AA9CkSV08FSFEgCSQ+yGBXNQnT86ezfw33uDhxx7j5dde\nK/+KXi9s3KjfkJcu1Qe3Ro2CrKw6W6touGpjfLzH5+HDAx9iOS2W7VjGpcJLdGraidQBqaQlppHY\nPjGkD2dWhr8wXqzcUH7+vH6tW5Z+7SsFgwbpED59uj6cLYSo1ySQ+yGBXNQXubm59O/enaz8fEbH\nxrJt//7yd8knTdJjrZs2hZQU/aY8YoTeKRPCD79hvFglQ7lSii9zv7wyOfPE5RM0b9SclH4pmHaT\nYV2HhcXhzMqoTBgvVhzKY4cMIePHPyZq4UL9KVhBgW5HmpamP/3q2bOuli+EqAESyP2QQC7qiydn\nz4Y33+SVwkKejI7GmDVL75IfOKDrQxcv1jvgrVvDe+/pXfHx43XNqBCVUKkwXsxPKN93dh+W08Jy\nWuw+s5voyGju73k/pt3k/l73ExMVU/tPpgEJJIwX8wJ5QBNAtWuHMWOG/sH79tvlcKYQDZQEcj8k\nkIv6oHh3fFt+Ph2B48BLNhu/HziQ6C++0FcaOhT++lfo1y+YSxUNVEBhvFipUP7mP99k+e7lpOek\ns+XoFgBGdBuBaTeZ0ncKLWNb1u4TaKCqEsaLeYH/aNSI7cOGkblmTXhNEhUiBEkg90MCuagPnpw9\nG9u8ecx1u6+6PLdVKzr+4hf64+muXYO0OtHQVSmMF/NCxMkIVAeFMhSJ7RP14cwBqXRu3rnW1hwq\n5s+fz1OzZnHQ46FpFW5/EegWFcXL8+Yxc+bMml6eEKIOBRLII2p7MUKIUjwezmRmcscbb/DiNWH8\nFDDA5eL4D34gYVxUS0ZGBlkbsnBNCDCMA0SCr4MPPoc5N8zh28e+5VdDfiVhvJIcDgfDk5JwxMVR\nEOBtCwBHXJy+vcNRG8sTQtRTEsiFqCu//S3Ex9Pa4WCiz3fdi68t8JDPx9w5c4KxOhFCHA4HSSOT\niFsVB54Ab+yB2CWxjIkcw9MPP10r6wtlNpuNhYsXM6xnT76KjKSyn0FXuS+5ECIkSCAXorbs2wd/\n+YtuVwZw5gx5t97KQzYb58u5ya8KC5n/9tscP368zpYpQo/NZmP1stUMTRhK3PIAQnlRDfmwhGG1\nOsEzJHm9ejruD3+ILT6eX377LX0iI1kZFUVhBTeVMC6EkEAuRE06dQr+53/grrt0u7Inn4Tdu/XX\n/vu/ebZrV1obBh3LuXlH4CGvV3bJRbUVh/JBPQdhc9qocKu2hoYEhRWl4Ouv4Re/gC5dICkJlizR\nbUrXr6fJhQv8IymJKX7KVySMCyFADnUKUXPWrYPkZL1TdtNNumVZairExwPXd1YpTy4woKK+5EL4\ncbHgIst3LsdyWnyw/wN8yodxzkA1U1BWm3AJ44E5eFC3Jk1Phx079Lj6ceP0az45+arWpDUyqVMI\n0SDJoU4hapvbDWvX6jfgN9/Ul911F/zyl+B06vHWv/zllTAOMHfOHGb6fH7DOMguuagat9fNO7vf\nIXVpKu1fas/MFTPZfWY3z9z9DN8+8i1j9o0hblkZ5SsSxivnzBl4/XW4+25ISIDnntMzAl5/HXJz\nYeVKmDbtujkBNptNh+2hQ0kptVMuYVwIUZrskAtRWUrBli16lPXChbo8pWVLePZZ/ZF1BbrHx3Pg\n6NFKP1xCp07sP3KkOisWIU4pxebDm7GcFou2LeJM3hlax7ZmWv9pmHaTwZ0HXxlfX2YrRAnj/rlc\nsHq1fs2vWwcej54LkJamP/3q1q3Sd1V6pzzD5cIhYVyIkCd9yP2QQC4CdvIktGunf33TTbBrF0yY\noHfHx46FRoGM/hCi+nac2oHltMhwZnDg/AFiomKY2Hsipt3k3hvvJToyuszbXRXKJ7iIWyVh/Doe\njz6caVmwbBlcugSdOukAbpr634AqTs4sDuWbsrIYnpQkYVyIECeB3A8J5KJSjh+HBQv0m/L27XDi\nBDRpAlu3QufO0Lx5sFcowsyxi8fIdGZiOS2+Pv41EUYEo7uPxrSbTOoziaaNKjeGpjiUZ23IImlk\nkoRx0J9+ffmlfr0vWKBf/82awdSpOoQPGwaRZRXfB87tdpORkYHD4ZA/dyFCnARyPySQC78+/VT3\nC8/KAp8Pbr5ZvyE/8gg0rcrcPSGq7kL+BZbtWIbltPjwwIcoFLfdcBum3WTGgBl0aFK1Q78SmkSk\nGgAAIABJREFUCovs26dDuGXpbkjR0XD//fo1f//9EBMT7BUKIRqwQAJ5oDPchAgtbje8955uUdin\nj/64eu9eeOYZ/abct2+wVyjCTKG3kHV71pHuTGf1rtUUeAvo3rI7zw97HtNu0rtN72o/hs1mC9+x\n7KdO6TMglgWffaYvGz5cH8KeMkWfCxFCiDomgVyEH6Vg82b9hrxoke6e8POfw0sv6Q4K+/ZVuUZU\niKrwKR8ff/cxVo7F4u2LOZd/jrZxbfnxLT/GTDS5o9MdVw5niiq4fFl3QUlPh/ff161JExPhD3/Q\nteGdOwd7hUKIMCeBXISX4jfi7dt1e7KJE3XHhDFj9Ncl9Ig6tPXkVqwci4ytGXx34TvibHE80OcB\n0uxpjO4+GltkGJeTVJfHA+vX6x+8V6zQobxzZ90RyTTBbg/2CoUQ4goJ5CK0HTsGmZm6N/jf/64P\nZj34oO6a8MADUhcu6tzhC4fJ3KoPZ+acyCHSiGRMjzH8ftTvmdhnIk2imwR7iQ2XUvD55yWHM0+d\nghYtdAA3Tf0JWISM3xBC1D8SyEXouXBBtyuzLN2+TCm47TbdvqxJE/j1r4O9QhFmzuefZ8n2JVhO\ni00HN6FQ3NHpDl4d+yrTB0ynXeN2wV5iw7Z7t369Z2ToMyCNGumJmaapJ2hKa1IhRD0ngVyEhoKi\n+XeNGsFbb8FTT0GPHvDv/w4OB/Su/kE4IQKR78ln7Z61pOeks2bPGgq9hfRq3YsXRryAw+7gxlY3\nBnuJDdvx4yWHM7/4QpebjRypB3VNniytSYUQDYoEchF0ubm5jLz7bjZ+8gkdOgTQxs3ng48/1m/I\nixfDK6/AzJm6JGXwYBg0SGrCRZ3yKR+bDm7Cclos2b6ECwUXaN+4PT+57SeYdpPbbrgt7A9nVqvl\n4sWLsHy5fs1/8EFJa9KXXoIZM3QpmhBCNEASyEXQzZ0zh5MHDzJ3zhxefu21im+Qnw8vvKBrw7/7\nDuLiYNIk3bYQoE0b/Z8QftRUL26lFDkncq5Mzjx68ShNopswqc8k0hLTGJUwiqgI+acWrp5UuTIz\ns3KTKotbk1qW7pSSl6dH1v/617okpV+/Olm7EELUplobDGQYxlvAeOCkUmpA0WUvAD8GThVd7Vml\n1Nqirz0D/AjwAk8opd4runws8BcgEpinlHqx6PIEYAHQGvgSeFApVVjRumQwUP2Sm5tL/+7dycrP\nZ3RsLNv27y97l/zwYdixQ3dDUUr3B+/eXXdImTgRGjeu+8WLBqsmplUeOn+IDGcGltNi26ltREVE\nMfbGsZh2kwm9JxBni6ul1TdMxWHcnZ1NhsuFIy4O29ChZYdypfSQrvT0ktakrVrB9Ok6hA8eLJ9+\nCSHqvXoxqdMwjGHAJeAf1wTyS0qpl665bj8gExgE3AB8APQq+vJu4B7gCPAFkKqU2m4YxiJgmVJq\ngWEYfwW+VUq9XtG6JJDXL0/Ong1vvskrhYU8GR2NMWtWyS75+fOwZIneGdu0Sb8hHz8OUVFQWKin\n6gkRoOIwnn0gG9cEF3Gr4hiaMLRSofxs3lkWb1uM5bTI/i4bgMGdB2PaTab1n0abOPlkpiylw/gS\nl4tGQAGQcm0o37Gj5HDmgQN6UubEiTqE33uvvOaFEA1KvQjkRQvpBrxTiUD+DIBS6r+Kfv8e8ELR\nl19QSt1b+nrAi+hd9g5KKY9hGHeVvp4/Esjrj+Ld8W35+XQEcoEBxbvkCxbA00/r4N2rl94Jdzj0\nQU0hquiqMD7JpYv2PBC3vPxQnufO453d75DuTGfdnnW4fW76tOlDmj0Nh91BQsuE4DyZBqKsMF6s\nAHgkJoYRXbvycGwsxjff6LaESUn6NT9pkrQmFUI0WIEE8mAUNj5uGMZDwL+AnyulzgGdgM9KXedI\n0WUAh6+5/A50mcp5pZSnjOtfxzCMR4BHALp06VITz0HUgLlz5jDT56Nj0e87Ar/0eHQt+ZQp8JOf\n6DflW2+Vj6dFtZUZxgGiwDXJRfbybJInJ7N62WoiIiPYcHADltNi6falXCy8SMcmHfnZoJ9hJprc\n3OHmsD+cWRn+wjhAI+Dv+fkYu3axt1kzEl56iUiHAzp2LOvuhBAiZNX1Dnl74DSggDlAR6XUDw3D\n+B/gM6VUetH13gTWFd3NWKXUrKLLH0QH8heKrn9j0eWdgXXFj+OP7JDXD7nHjjE9IYG1hYWUHoNy\nDrjRXy25EFVQbhgvzQMxG2Job29P4Y2F5F7KpWl0U6b0m0KaPY0R3UYQGRFZ52tvqCoK46UVAlP8\n1ZQLIUQDFMgOeZ2OLFNKnVBKeZVSPuD/0DXjAEeBzqWuGl90WXmXnwFaGIYRdc3lor4r1OduX3vu\nOTZcE8YBWgIPeb3MnTOnzpcmQlOlwjhAFOTfk8+hdofwHvaSMSmDE784wdsT3yape5KE8QAUh3HP\nRx+xtIIwDhANLHG5cGdn6xDvdtfFMoUQot6o00BuGEbpzyEnAVuLfr0KmGEYRqOi7ik9gc/Rhzh7\nGoaRYBhGNDADWKX0tv4GIKXo9jOBlXXxHEQVnD0Lb7wBQ4fCuHHk5ubyvwsWcKGcq/+qsJD5b7/N\n8ePH63SZIvRUOoyX5oNLH1xi/nPziQrRzrBut5v58+fXSvB1u938csQIUrOyWJmXR2WPYTZCQrkQ\nInzVWiA3DCMT+BTobRjGEcMwfgTMNQzDaRhGDjASeBJAKbUNWARsB94Fflq0k+4BHgfeA3YAi4qu\nC/A08JRhGHvRNeVv1tZzEVX0/vvwwAPQoQM89phuXTZ6NHN/9ztm+ny0KudmHZFdclF9VQrjUFJT\nfkDXlIdaMCzevX5q1qyaDb5HjsAf/8jFHj348+bNpHg8BFpl3wjIcLnYlJVFRkZGzaxLCCEagFqt\nIa+PpIa8Fnm9sGGD3glv1Ah+9zu9M56aqtuWDRxI7vHjV3VWKc9VHVekllxUwfz58/nR4z/C+4SX\nSm/TllYAUa9GMe+1ecycObPG1xcMAfUCr4xrW5MqhW/QIN76/ns2HjrEm3l5FZarlFZmK0QhhGig\n6m0NuQhBSsFXX8FTT0HnznDPPbB2rf7aL36hJ2m+9JIeb20Y13VWKY/skouqUkrxxdEv+LLtl0T+\nW6QO44HuO3ggblUcSSOTcDgctbHMOnftIcumVLFEpKAAli2DKVOgfXv48Y/h6FH47W9h924itmxh\nZk4OrmHDSImLo6CS65MwLoQIZ7JDLqru4EEYNw527gSbDe6/X++E338/xMaWeZPu8fEcOFr587cJ\nnTqx/8iRGlqwCGV7z+7FyrGwnBZ7zu4hOjKa+268j4NrDrLr213kTcyrXNlKBX3JG6KKeoFXGIR9\nPvjoI70TvngxXLgA7dqVfPp1223XtSYNpMuKhHEhRCiqN4OB6iMJ5NVw+rQeYx0ZCY8+Ch4PpKTA\nfffp/7cqrypciNpx8vJJFm5diOW02HJ0CwYGw7sNx7SbTOk7hZaxLQOrJQ+zMF6s3ECck6PH12dm\n6hrxxo1h8mQdwpOS9NTc2npsIYRo4CSQ+yGBPEAuF6xcqXfG3ntPh/B774V33w32ykSYulR4iZU7\nV5LuTGf9vvV4lZeb2t+EaTdJtacS3yz+uttUtg95OIbxYsXBuONtt/H6mDFELlgAW7fq0H3vvTqE\nT5igQ3kNrUHCuBAilEkg90MCeSX4fHp8Neid76VLIT5ej643TUhMDO76RNhxe92s378ey2mxYucK\nXG4XXZp3wTHAgZloMqBdhTPB/IfyMA/jxXyUHCzy3XUXEWlpMG0atGlT42uRMC6ECHUSyP2QQF4O\npeCLL/RO+KJF8Nln0LUrfP653iUfNqwkpAtRB5RSbDm6BSvHYuG2hZxynaJlTEum9Z+GaTcZ0mUI\nEUZgfyfLDOUSxq/iAR6LieHs8OE1GpRrvMOLEELUc4EE8tCceiEq7+RJeP11HcT37NHtCsePh7w8\n/fVBg/zfXogatuv0LiynRYYzg33n9hETFUNyr2RMu8m4nuOIjqxKD0PNZrOxetlqHcqXZ+Oa4CJu\nVWiFcYCMjAw2ZWVx0OMJKIyDflN4JT+fbkW9wGuq5aPNZmPh6tVMT06mW1YWwyWMCyHEFbJDHo5O\nntRdEnr2hAMH4MYbYfhwXY4yZQq0aBHsFYowc/zScRZsXYDltPjXsX9hYDAqYRRpiWlM7juZZo2a\n1ejjFe+UZ23IImlkUkiFcajeDnltl5K43W4yMjJwOBwh9WcuhBDXkpIVP8I2kF+6BCtW6J3w9et1\na8KVK/XXTp2Ctm2Duz4Rdi4WXGT5zuVYTosP9n+AT/m4peMtmHaTGQNmcEPTG2r18UM9GLp372bR\n/fczdN8+ulTy33mp6xZCiJojgdyPsAzkTz2lJ2a6XNClS8nhzAEVH4QToia5vW7e2/ce6TnprNq1\nijxPHgktEnDYHZh2k75t+wZ7iQ3b6dO6T3h6OmzeDMD2Fi3YcPkys9xu6QUuhBB1SGrIw5lS+kDm\nihXw+9/rnuGtWsGDD+oQPmSIHM4UdUopxebDm7GcFou2LeJM3hlax7bmBwN/gJloclf8XRjXDJUR\nAXC5YNUq/enXu+/q1qT9++vXf2oqPTt14vnkZN6VXuBCCFFvyQ55qNi5U78hZ2TA/v0QE6M7pNjt\nwV6ZCFM7Tu3AcurJmQfPHyQ2KpaJfSZi2k3u7XEvtkgJfVXm8cCHH+rX/LJluiStUyc9OTMtTbcm\nLfVDjvQCF0KIuiclK36EVCBXSr/pvv++HtwREQGjRumd8MmToVnNHoQToaem66iPXTxGpjMTy2nx\n9fGviTAiGN19NGn2NB7o8wBNGzWtgVWHKaXgX//SIXzBAjhxApo317MCTFO3Jo2MLPfm0gtcCCHq\nlgRyPxp8IL94Ue+IWRaMHAnPPAP5+fDXv+oBHjfU7kE4ETpqqtPIhfwLLNuxDMtp8eGBD1Eobr/h\ndky7yfQB0+nQpEMtrD6M7NunX++WBbt3Q3S0PpRtmvr/MTGVvivpBS6EEHVHArkf9TWQ5+bmMvLu\nu9n4ySd06FBGgFmzBv75T10rmpcHCQnwi1/A7Nl1v1jR4F01IKcKvbgLvYWs27OOdGc6q3etpsBb\nQI+WPTDtJmaiSa/WvergWYSwkydh4UIdwrds0ZeNGFHSmrRlyyrfdXEo35SVxfCkJAnjQghRSySQ\n+1FfA/mTs2cz/403ePixx3j5tdf0x9M7dkC/fvoKI0eC0wnTp+sa0TvvvKpGVDRMwWi9V9VplT7l\n4+PvPsbKsVi8fTHn8s/RNq4tMwbMwLSbDOo0SA5nVsflyyWtSd9/H7xeXQtumro2vHPnGnuoUG/5\nKIQQ9YEEcj/qYyDPzc2lf/fuZOXn89NGjXj/0UdpsmoVHD4Mubm6R/iRI9C+PcibZ8hwu90kJ08n\nK2sTSUnDWb16Ya2HozLDeLFyQvnWk1uxciwytmbw3YXviLPFManPJEy7yejuo+VwZnV4PHouQHq6\nDuMulw7epimtSYUQooGTQO5HfQzkT86eTff/+z9+5vEA4DMMIsaMKfl4Oi4uyCsUNa04jGdnu3G5\nMoiLczB0qK1WQ7nfMF6sKJTf3ut2xj45lsxtmeScyCHSiGRMjzGkJaYxsfdEGkc3rpU1hgWldBmK\nZemylFOndAnK1Kn6NX/33dKaVAghQoAEcj/qWyAv3h3flZ9PW+ACcGdMDBsOHCi7llw0eFeH8SVQ\n1O8iLi6l1kJ5pcJ4MR9g6P/u6HQHaYlpTOs/jXaN29XomoKtzss2du8uOZy5bx80agTJyboEbexY\n/XshhBAhI5BALtswQTZ3zhxm+nwUD65vDoz1+Zg7Z04wlyVqSdlhHKARLtcSsrP1191ud80+ZmXD\nOOh/FXwQszaGFitb8OjNj4ZkGJ+enMxTs2bpriM1+Od9lePH4c9/httvh969Yc4c6NYN3npLty1c\nvBgmTpQwLoQQYU52yIOoeHd8W34+HUtfDgyIjWXb/v2ySx5Cyg/jpdXsTnnAYby0Shz0bIhqvfXf\nxYuwfLneCf/gA/D54OabdTnKjBl6gI8QQoiQJzvkDUTx7njHay7vCDzk9coueQipXBiHmt4pz8jI\nIGtDFq4JAYZxgChwTXCRtSGLjIyMaq2jvrh2OE5TYInLhTs7u3o75W43vPOODtzt28PMmbpE5Zln\nYPt2+Oor+PnPJYwLIYQok+yQB0l5u+NXvo7skoeKyofx0mpmp3zv6b3c98x97G26F9U8wNd6iO2Q\n1/j4eKVg82a9E75oEZw5A61b6wFdpgmDB0trUiGECGOyQ94AlLc7Xkx2yUND1cI4VGen/GzeWd74\n1xsMe3sYPV/ryZ74PTSPaI7tSxt4KnknYRTGQX9XKr1TvmMHPP889OihO6L8/e8wejSsXg3HjsH/\n/i8MGSJhXAghRKXJDnkQVLQ7fuV6yC55Qzd//nxmzXoKj+cg0LQK93CRqKhuzJv3MjNnziz3Wnnu\nPN7Z/Q7pznTW7VmH2+emb5u+mHYTh91BfJP4yteSh1kYL63cnfJjxyAzU++Gf/21bks4erTeCZ80\nCZpW5XsrhBAilEnbQz/qQyB/cvZsePNNXiksrPi60dEYs2bp6Z2ixtV26zu3201S0hN89tlg3G6T\nwD6U8l+24vV52XBwA5bTYun2pVwsvEjHJh1x2B2YdpOBHQZeNTkzkD7k4RjGixWH8uZ33snfU1OJ\nWrAAPvxQl6jcdlvJ4Uz5IVkIIYQfEsj9qA+BvHt8PAeOHq309RM6dWL/kSO1uKLwVJuTMk+f1mXF\n6enw6af6soiIrfh8vYHKPEbZYVwpxdfHvyY9J50FWxeQeymXZo2aMaXvFEy7yYhuI4iMiCz3Xqsy\nqbOhqkoYL+Yt+i8aUN27Y6SlgcOhWxcKIYQQlSCB3I/6EMhF8NXGpEyXC1au1FUN772np6IPGKA3\nVFNS3Dz+eGVrya8P4/vP7SfDmYHltNh5eie2CBv39byPtMQ07u95P7G22MCe+7WhPMTCOOhyoadm\nzeKgx1OlYqFCIDkyEse8ecx8+OEaXp0QQohQJ4HcDwnkoiYnZXo8kJWlQ/iyZXD5MsTH681U04TE\nxIoe91ol63h74Wss370cy2mx+fBmAIZ1HYZpN0npl0Kr2FbV+zMoDuUTXMStCq0wDtXbIa9S1xUh\nhBCiFAnkfkggD2/+Q3HlQrlS8MUXOoQvXKgHLjZvDlOn6hA+bJg+81cWl8tFl259OHO6J6i11z0+\ntntpcruTux8bxAcHP8Dj8zCg3QBMu0nqgFS6tuhaQ38SJaE8a0MWSSOTQiqMF6tODbmEcSGEENUh\ngdwPCeThq7qTMvfu1SHcsmDPHoiOhvHjdQi/7z6IianE409O5qP9H5GXDxy+E9zrICISEt6Fm2ZB\n3xNgg5jCGH467Kc8NPAhEtsn+r/jaqjtQ631gfvUKf5v5Eju3rEDu8+Hv2aEEsaFEELUFAnkfkgg\nD0+B9QMvCeXz5i1k2TIblgWff65bS48YoUP4lCnQokUAj1+6btsA3msEbdpCv0JoclIXLUcBCmKX\nxTIsYVhI7lrXifx8WLtWn6pdswYKCzkaF8dGt5sUt7ucH8UkjAshhKg5Esj9kEAefqo2nMdNREQO\nSg1EqUhuukmH8NRUXSMe8OOX19nEC+yOghMRMLQQihukhOAhy1rn88GmTfojjCVL4MIFPcZ+xgww\nTdw33cT0CRNqblKnEEII4YcEcj8kkIeXqk/KBFBERS1h0KBP2Ljxj1UKaW63mzHTxvCJ+gT3QDfX\n1Ut4ASdgpySMF5NQXjGlICdHh/CMDDh6FJo00cN60tJg1CiIKvkJqKyacgnjQgghakMggTyQKSVC\nNDgZGRlkZW3C5cogsDAOYODxjOXzz/9JRkZGQLe8VHiJ9Jx0bn3lVjYmbsR9cxlhHHQIH8j1YRwg\nClwTXGRtyAr48UPeoUPwX/8FdjsMHAivvAI336ynaZ44Af/4B4wZc1UYB7DZbDp0Dx1KSlwcF5Ew\nLoQQIvhkh1yEtOrtkAfWCtHtdbN+/3osp8WKnStwuV10adaFyF2R5Obkkp+U739k/bVkh/xqZ8/C\n4sV6Nzw7W182eLCuJZo2Ddq0qfRdFe+Ub8rKYnhSkoRxIYQQNU5KVvyQQB5+3G43Y8Y8yief3IXb\n/SMq98FQZVsgKrYc3UJ6TjqLti3ilOsULWNaMq3/NEy7yZAuQ/B6vBWPrL+WhHEtLw9Wr9YhfN06\ncLuhb18dwh0OSEio8l2HQ4cZIYQQwSOB3A8J5OHj++9h+XKd5bKyFD6fgWHsRalu+E/FFYfxXad3\nYTktMpwZ7Du3j5ioGCb0noBpNxl741iiI6Ovur7fg53XCvcw7vXChg36G7d0KVy8CB076hO1aWm6\nRMXw17xQCCGECD4J5H5IIA9thYV6bL1l6TH2+fl6E9U0YdKkPMaM7V3OUJ5iBWDcR+s2e/ju4E7i\n4uKufOX4peMs2LqA9Jx0vsz9kggjglEJozDtJpP7TqZZo2Z+11apUB6uYVwp+Ppr3aZwwQLIzYWm\nTSElRX/zRoyAyLIK7YUQQoj6KZBAHkhFqxD1ks8HmzfrEL5okS41bt0afvhDneXuugs8HjfJkyfh\nansamp6Gw+P0UJ7rJmWOg86f4YqBydMnk5GZweq9q7GcFlkHsvApH7d0vIU/jfkTMwbM4IamN1R6\nnTabjdXLVutQvryMUB6OYXz/ft0dxbJg506w2fSUJdPUU5diY4O9QiGEEKLWyQ65aLC2by/pdnfw\noM5uEyfqqoYxY3S2g3KG8mTEwnd3lgrlRWG8y2fgyAMg8mgkqpPCF+kjoUUCpt3ETDTp06ZPtdZd\n5k55OIXx06f1T06WpX+SAhg6VH/jUlKgVavgrk8IIYSoAVKy4ocE8obt6FHd2c6y4JtvICIC7rlH\nb6g+8ICuciit3DIRL6VC+UqwTYBbN8OYwpIznwqi9kVxq/tWPvrHR0RHX10XXh1XrWuCi7hVIR7G\nXS5dQ2RZuqbI44H+/XUIT02Frl2DvUIhhBCiRkkg90MCecNz4YI+22dZ+qyfUnD77TqET58OHTqU\nf9v58+cz66ez8Dzhub5k3AusaQQtFdzshiZlvBYKIOrVKOa9No+ZM2fW5NO6EsqzNmSRNDIp9MK4\nxwNZWfobt3w5XLqkx5ympupvXmKiHM4UQggRsiSQ+yGBvGEoKNBd7tLT4Z139O979NAbqg4H9OpV\nufup1EHK4pfAtdmwDspIQq71nlLwr3/pb9zChXpIT/PmMHWqDuHDhumPNYQQQogQJ4HcDwnk9ZfP\np+e9WJae/3L+PLRtCzNm6Cw3aFDVNlTdbjdjp47lY8/HFN5aWLk25OFU010T9u7V3zjLgj17IDpa\nH8o0TX1IMyYm2CsUQggh6pR0WRENitNZcjjz8GFo3FjXg6elwejR100/r7QCTwHr9q7Dclp8ctsn\nFHoLMS4aqMbKfyiXMF45J0/qXfD0dPj8c/3T0vDh8PTTMGUKtGgR7BUKIYQQDYIEchEUhw+XdLtz\nOiEiwse998KLL0YwcaIO5VXhUz6yD2VjOS2WbF/CufxztGvcjkdufYQZ/WbwH4//Bx8f+Fj6gBcJ\nuGTm0iVYsUJ/49av10N8broJ5s7VteHx8bW/aCGEECLESCAXdebcOViyRGe5TZv0ZXfc4aNP/9fZ\nc+A/8UUmMnVq1UKw84TzyuTMw98fprGtMZP6TsK0m4zuPpqoCP1X/Z1l70gf8CJut5vpyclsyspi\nZWYmC1eX85zdbh2+LUuHcZcLunSBX/5Sl6QMGFD3ixdCCCFCSK3VkBuG8RYwHjiplBpQdNkfgWSg\nENgH/EApdd4wjG7ADmBX0c0/U0o9VnSbW4G/A7HAWuDflFLKMIxWwEKgG3AQmKaUOlfRuqSGvG7l\n5+tDmZYFa9fqSZq9e+scN3Wqm//3y6q3/jt84TAZzgwsp4XzpJNII5J7b7wX024ysfdEGkeXvc0e\n9n3AKQnj7uxsMlwuHHFx2IYOLQnlSsGWLbocZdEiOHUKWraEadP0N2/IEDmcKYQQQvhRLw51GoYx\nDLgE/KNUIB8DfKiU8hiG8QcApdTTRYH8neLrXXM/nwNPAFvQgfxVpdQ6wzDmAmeVUi8ahvFroKVS\n6umK1iWB/Ho13enD69U74Jal2xVeuKBbExYfzrz11uLJmYGH4nN551iyfQmW0+KjQx+hUNwZfydp\n9jSm9Z9G28ZtK/2cw6oPeCmlw/gSl6t4LBIpcXF0veUW/jJsGJELF8K+ffowZnKy/saNG6cPawoh\nhBCiQvUikBctpBvlB+1JQIpSyizveoZhdAQ2KKX6FP0+FRihlHrUMIxdRb/OLbreRqVU74rWJIH8\nam63m+Tk6WRlbSIpaTirVy+sUiBVCr79VofwzEw9wKdJE5g8WR/OHDmy5HCm31aEZYTyfE8+a3av\nwXJarNmzhkJvIb1b98a0mzjsDnq06lH15x7KfcDLUFYYL+ZDn3X1AYwaRcSDD+pvYLNmQVmrEEII\n0ZA1lC4rP0SXnBRLMAzja+B74HmlVDbQCThS6jpHii4DaK+Uyi369XGgfXkPZBjGI8AjAF26dKmZ\n1YeA4jCene3G4zlIdraD5OTpAYXygwdLDmdu365D97hx8Kc/6Y3VuLgyHtNfX/AocE1y8dHyjxg8\nczADpg1g+c7lXCi4QIcmHZh922zMRJNbO96KUc2hMjabjdXLVodWH3A//IVx0GHcA/woJoaLNhsL\nTTPk/0yEEEKI+iAoO+SGYTwH3AZMLqoHbwQ0UUqdKaoZXwH0B3oBLyqlRhfdbijwtFJqvGEY55VS\nLUrd5zmlVMuK1iQ75FrpMO5yLYGiwoW4uBSGDrX5DeVnzug+4ZYFH3+sLxsypLguHNq08fOYFQ3p\nKaYAAyK9kTgGOnjwpgcZlTCKyIjIajzr8OV2uzHvv58BmzbxXGEh/v4Ui8tXrqopF0JXvwFsAAAZ\nxklEQVQIIURAAtkhr/NTWYZhPIw+7Gmqop8GlFIFSqkzRb/+En3gsxdwFCjdRy2+6DKAE0WlKsWl\nLSfr5AmEgLLDOEAjXK4lZGfrr7vd7iu3ycvTZ/smTICOHeEnP9HB/D//Ew4c0MH8Jz+poTAOemqm\nF6JXRnPy7ZOM6DJCwnhV+Hx4Nm4kq0cP5q1fz28qCOOg/zYscblwZ2frHfVSfw+EEEIIUfPqNJAb\nhjEW+BUwQSnlKnV5W8MwIot+3R3oCewvKkn53jCMOw1dn/AQsLLoZquAmUW/nlnqcuFH+WG8WEko\nHz8+lXXrPDz8MLRvD9Onw5dfwhNPwFdfwbZt8Oyz0K1bJR4zkDBeLBLyJuSRfSCb5MkSDAOyfTs8\n9xyqRw+iRo5k5OHDBNLaXUK5EEIIUXdqs8tKJjACaAOcAH4LPIN+rz9TdLXPlFKPGYYxBfgd4Eaf\nKfutUmp10f3cRknbw3XAz4rKXFoDi4AuwCF028OzFa0rnEtWKg7jpXnQ5fytaNZMkZJiYJp6EGNk\ngBvV8+fPZ9ZPZ+F5wuP/IctTAFGvRjHvtXnMnDmz4uuHq6NH9Ylay4JvvoGICI7268f/t307c30+\nmlbhLi8C3aKieHme/NkLIYQQgag3XVbqo3AN5IGF8WJeoqPnMmzYt6xd+88q1RJ7fV7e3/s+s/57\nFseaH4NA7yLM+oMH7MKF/7+9e4+OqrzXOP79hcSQIMhFtFgEEesNoRahpVaUCiLKRbRBcKLGttal\nPb23p8fTdrUuu+yq51R76tG2q1oraoabSosiVkg5QFupChUjggVRCx4EEeUWDCF5zx/vnsMQZiaT\nIZk9yX4+a83KzN57Zr95s2fy5M178XNLVlfD0qV+upuRI32H/unTaejTJ+NAzkzUl1xERCR3CuQZ\nRDGQ5xbGE7Ib6JnMOcfqrauprq1m1iuzeGfvO/Qo7UH3Ld3ZsXYH9RfUZ9dtRWE8tfp6WLTIh/An\nn/SPBw/280vGYnD66Ycd3tLsKilPgcK4iIjI0SjoQZ2SWkNDAzNnzmyXvrrxeJyammXU1cVpfZ+R\nUurq/PPj8XjGIze9v4kfL/sxZ913FiPuH8G9z9/LqP6jmDdtHu98+x3e+PkbjDl2DOXzy32PmEwU\nxg/X1ORXW7rpJr/K0pVXHnq8ciVs2AC33XZEGAc/veOcJ5+kZPRoKsrLqW/hVArjIiIi+aUW8gLQ\nVovzpLN/fwMXXPBT1qy5gMbGMfgpTLKVuYX83X3vMnftXKprq3luy3MAXDjwQiqHVlJxdgW9y3of\ndnxWAzwVxg+prfUt4fE4bN4M3brB1Km+S8q4cdCKusmmpVxhXEREpG2oy0oGhRbID+9OEqe8PNaq\n7iHpOAcvvOCz3OzZsH07FBfvxbk/09h4MZDNEuipw/i+A/tY8NoCHq19lGdff5aDTQc554RzuHbo\ntVwz9BoGHJd58aXWrtQZOZs3H1ptqbbWj6K99FIfwq+4wofyHGUK5QrjIiIibUeBPINCCuRHszhP\nOhs2+BxXXQ0bN0JpKUya5LPcJZc0UFGRbV/yw8thXYwlm5ZQXVvN/HXz2dewj/49+hM7J0blsEqG\nnTis9d9781Ae5TD+/vvw2GP+B7d8uf+LatQo/4O7+mo44YQ2O1WqUK4wLiIi0rYUyDMolECeeaBl\n60L5tm0wZ47Pcs8/D2YwZozPcp/7HPTseejYhoYGJk2axpIl+2hqeorUobyeoqJJjB1Xzm2/+Tfm\nvDqH2Wtns33fdnp27UnFWRVcO+xaRg8cTZHlPgzhsFA+pY7yBREL4x9+CAsX+h/cwoVw4IDvA54Y\nnDl4cLudOjmUx+vqiCmMi4iItCkF8gwKIZBnN+tJ5lC+dy/Mn++z3JIl0NgI557rQ/iMGdC/f4qX\nDM49cepEapY9R1P9SDi4iOZ/DND3Iuzcv1N2XjF1Xes4pssxTD59MpVDK7n8Y5dTWpzLZOLpyzP5\nqsnULK1h7GfHdv4w3tjoB2NWV/vpCnft8oM0Z8zwP7zzzvN/UeVBIpQvq6nhorFjFcZFRETakAJ5\nBmEH8tZNQXh4KIcSnn3WZ7nf/94vZz9woG9MrayEIUOyOHdSizRzy+Cfo6BhEXT7AM6phmG3wUf3\ngIOid4s4q+4slt6zlL7d+7ZdJaQoVzweJxaLdc5A6BysWeN/cLNm+QV8jj0WrrrKt4Z/9rNQnO3y\npW2r09e9iIhISBTIMwgzkOc2H3g9paU/4IQTLmb//gns2GH07u27FVdWwvnnQ1EWvUZS9tluBJ4r\ngX7dYdAuKGqEnQY9nZ8QM8p9utvCm28eGpz56qs+dF92mf/BTZ4M5eVhl1BERETaiQJ5BmEF8qNb\nnAegno985EXuu+9TTJpUzDHZTJKSfO5MUw3uNXipxIfxifXQJWlfJw/lbd5C/N57MG+eD+F//rPf\n9pnP+BA+bRocf/zRn0NEREQKnhYGKkBHtzgPwAF27JjCnj3VrQrjANXV1Sx5bQl1V6WZ97vMwbEH\njgzjAMVQN6WOmqU1LS4M1NEk+lB/68Yb/QDHXBdl2r8f5s6FKVOgXz+45RYfzO+4AzZt8sH8llsU\nxkVERCQltZDnSb6XrwdYv2M91S9XE6+Ns+mDTb6LitG6P8M6aQv5Uc8y0tgIf/qTbwl/4gnYswdO\nOgmuuca3hp97bt4GZ4qIiEjhUZeVDMLsQ15XV8eAAUN4773TgHRTDjZXD0yiT5+N/POfaylvod/x\n1j1bmf3KbKprq1m1dRVFVsTFgy5mxtkzmHX7LJ57/bn0K2Q2F4Ew3qp5uJ2D1asPrba0dSv06OHn\nlqys9HNNdmn+LwYRERGJIgXyDMJuIV++vJ79+0vwzdUtz7ICFUAXysoauPDC0pQt5LvrdzN/3Xyq\na6upeaOGJtfE8H7DqRxayYxzZnBS95MOlaGlZesTIhTGE9KG8k2bDq229Nprfrn6iRN9CJ84EcrK\nwvhWREREpIApkGcQViCfOXMmN974LQ4efBPoCkwHGkgfyhNhvASYA3xIcfEpPPDA3VRVVXGg8QDP\nbHyG6tpqFry2gA8PfsignoOoHFpJ5bBKzjz+zJTlyCqURzCMJyRCeZ9PfYrfTp1Kl9mz4bnn/M4L\nL/QhvKICevfOZ9FFRESkg1EgzyDsFvJDfciLSB/Km4fxJsrLK7hgdDH//suvM+fVOcx9dS479++k\nT1kfpg+ZTuWwSj7d/9NYFv2WM4byCIfxhEbA4avFDRmCXXed7xs+YEB+CisiIiIdngJ5BoU1D3mq\nUH5kGO968qWcdOlWGofU89autygrLmPqmVOpHFrJ+MHjKenS+tCcMpQrjP+/g8A3u3bl7Ysu0gqW\nIiIi0moK5BkUwkqdkyZNY8mSfTQ1PcXhoTwOxIAS6H43DJ0NQ++AfnspsiIuOfUSKodWMvXMqXQv\n7d4mZUleubN8gcJ4sqwGeoqIiIik0JpAHs563RHniuugbCXUXwYHF+FbwqdD6QA4exAM7QmDTgVz\nsLWIM946gyV3LaF/r/5tWo6SkhKefOJJJl81mZp7ahj92c4VxsHP/76spoY3Dx5s9ezvpUC8ro5T\navwc7FVVVe1RRBEREYk4LQyUR4kW6b+89ReavrYXBq6Eksug63twNfCve+GKv8Nx/4QVA+H3pdC7\nic3Pb+bG62/MfeGaDBKh/IH7Huh0YRwgNnYsd552Gv9b1PpLvR6IlZdz0dixxGKxti+ciIiICArk\neXNEn+1SILYfBqyExmt8CF91C9y/An59Mry1DSbXQynUXVnHijdWMPmqo1hNMoOSkhKqqqo6Txjf\nuxcefRQmTKDklFO4cf16junWjQdLSqjP8iXUXUVERETyRYE8T+LxODVLa6ibkjSrSReCUP43eKgH\nLLoDtv/QP47tP7SMfSdevr7NNDTAwoUQi8GJJ8J118H69fDd78Irr9D/vfd46uKLqSgvbzGUK4yL\niIhIPmlQZ55knGqwEYiXwRvFMOjg4WEcOu3sJ0fNOVi50i/YM2cO7Njh5wefNg2uvRbOPx+Suqq0\nZh5yhXERERE5GpplJYPQpz3MFMprgaEojLdk/XofwuNxv4pm164wZYpftGfCBDjmmLRPzWmlThER\nEZFWak0gV5eVPEoMoBw9aDTl88v9ZNcJXYBzURhPZ+tW+PnPYcQIOOss+MlP4NRT4Xe/g23bfAv5\nlCkZwzj4n8GcJ5+kZPTow7qvKIyLiIhIWBTI8yxjKE+mMA67d8PMmXDJJdC/P3zrW76byt13w5Yt\nsHgx3HAD9OjRqpdtHsr3oDAuIiIi4VEgD0GLoTzKYfzAAViwAKZP94Mzb7gBXn8dvvc9WLcOVq2C\nb34T+vU7qtMkh/JTiosVxkVERCQ06kMeoigtX59RUxP89a++X/jcubBzJ/Tp40P5tdfCqFFg1i6n\nbmhoIB6PE4vFolHXIiIikhca1JlBIQVyiMby9WmtXXtocOZbb0FZGUyd6gdnjh8Pnf37FxERkU5L\ngzo7kOTuK8X3FIcSxhsaGpg5c2a7LDp0hLffhp/9DD7xCTjnHLjzTjjzTHj4YT84Mx6HiRMVxkVE\nRCQyils+RNpbIpSH0XUiMQ3gspoa/jBrVvv0o961Cx5/3LeGL13qB2aOHAm/+MWhvuIiIiIiEaUu\nKxGWPCd3vK6OWFvONFJfD08/7UP4U0/5x6ed5rujxGJw+ult802IiIiIFKDWdFlRC3lEpVog57G6\nOipWrGD65Mm5hfKmJlixwofwefPggw+gb1+46SY/OHPkyHYbnCkiIiLSUSmQR1C61SpzDuW1tfDo\nozBrFmzeDN26wZVX+tbwceOgWJeZiIiISDoa1BkxmZaOh0OhvCEI5WkHem7e7AdkDhvmb3fdBUOH\n+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8aefe7908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lm_final = ols('S ~ X + C(E)*C(M)', data=salary_table.drop([32])).fit()\n",
    "mf = lm_final.model.data.orig_exog\n",
    "lstyle = ['-','--']\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='Experience', ylabel='Salary')\n",
    "\n",
    "for values, group in factor_groups:\n",
    "    i,j = values\n",
    "    idx = group.index\n",
    "    ax.scatter(X[idx], S[idx], marker=symbols[j], color=colors[i-1],\n",
    "            s=144, edgecolors='black')\n",
    "    # drop NA because there is no idx 32 in the final model\n",
    "    ax.plot(mf.X[idx].dropna(), lm_final.fittedvalues[idx].dropna(),\n",
    "            ls=lstyle[j], color=colors[i-1])\n",
    "ax.axis('tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##### Interaction Plot Salary | Experience\n",
    "\n",
    "From our first look at the data, the difference between Master's and PhD in the management group is different than in the non-management group. This is an interaction between the two qualitative variables management, M and education, E. We can visualize this by first removing the effect of experience, then plotting the means within each of the 6 groups using interaction.plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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umnVVkiSpLRjIpYzddBOcdlq6++af/pR6xyVJUumwZUXKyPvvw5QpcPTRsMMO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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8aef3f208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "U = S - X * interX_lm32.params['X']\n",
    "U.name = 'Salary|X'\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax = interaction_plot(E, M, U, colors=['red','blue'], markers=['^','D'],\n",
    "        markersize=10, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Example 2: Minority Employment Data - ABLine plotting"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "TEST  - Job Aptitude Test Score\n",
    "ETHN  - 1 if minority, 0 otherwise\n",
    "JPERF - Job performance evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from urllib.request import urlopen\n",
    "url_base = 'https://raw.githubusercontent.com/thomas-haslwanter/statsintro_python/master/ipynb/Data/data_others/'\n",
    "inFile = 'minority.table'\n",
    "url = url_base + inFile\n",
    "minority_table = pandas.read_table(urlopen(url))\n",
    "\n",
    "#minority_table = pandas.read_table(r'.\\Data\\data_others\\minority.table')\n",
    "minority_table.to_csv('minority.table', sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2d8af7ccfd0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ta9oqVBmaEUEZAADUDDPTwNCAuvq7lLgycckyDGsxJa5IqKu/SwNDA9zGGpFiBTwAAKgp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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8af5d54a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor_group = minority_table.groupby(['ETHN'])\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='TEST', ylabel='JPERF')\n",
    "colors = ['purple', 'green']\n",
    "markers = ['o', 'v']\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
    "                marker=markers[factor], s=12**2)\n",
    "ax.legend(['ETHN == 1', 'ETHN == 0'], scatterpoints=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.517\n",
      "Model:                            OLS   Adj. R-squared:                  0.490\n",
      "Method:                 Least Squares   F-statistic:                     19.25\n",
      "Date:                Sat, 04 Feb 2017   Prob (F-statistic):           0.000356\n",
      "Time:                        17:09:34   Log-Likelihood:                -36.614\n",
      "No. Observations:                  20   AIC:                             77.23\n",
      "Df Residuals:                      18   BIC:                             79.22\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.0350      0.868      1.192      0.249      -0.789       2.859\n",
      "TEST           2.3605      0.538      4.387      0.000       1.230       3.491\n",
      "==============================================================================\n",
      "Omnibus:                        0.324   Durbin-Watson:                   2.896\n",
      "Prob(Omnibus):                  0.850   Jarque-Bera (JB):                0.483\n",
      "Skew:                          -0.186   Prob(JB):                        0.785\n",
      "Kurtosis:                       2.336   Cond. No.                         5.26\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm = ols('JPERF ~ TEST', data=minority_table).fit()\n",
    "print(min_lm.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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lNm3aFNQ8AAAACB1fQ0Cvby7Xb1cVq+JIrcYO6KqHr8vUhME9ZFmWLZksy9psjMlq6bhQ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\nyQCikXeF9yvLLZpiAkaeFZ4gJ4puFGU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jzHVtnpqslh/uAwBcILZeAEDsSDHG\nfNDm8f9I+pGkvzDGrJBUL6lWbLsAgIuC28MBAAAAQbD1AgAAAAiCogwAAAAEQVEGAAAAgqAoAwAA\nAEFQlAEAAIAgKMoAAABAEBRlAAAAIIj/A0qon3Qog6+PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8af7f0ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='TEST', ylabel='JPERF')\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
    "                marker=markers[factor], s=12**2)\n",
    "ax.legend(['ETHN == 1', 'ETHN == 0'], scatterpoints=1, loc='upper left')\n",
    "fig = abline_plot(model_results = min_lm, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.632\n",
      "Model:                            OLS   Adj. R-squared:                  0.589\n",
      "Method:                 Least Squares   F-statistic:                     14.59\n",
      "Date:                Sat, 04 Feb 2017   Prob (F-statistic):           0.000204\n",
      "Time:                        17:09:35   Log-Likelihood:                -33.891\n",
      "No. Observations:                  20   AIC:                             73.78\n",
      "Df Residuals:                      17   BIC:                             76.77\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.1211      0.780      1.437      0.169      -0.525       2.768\n",
      "TEST           1.8276      0.536      3.412      0.003       0.698       2.958\n",
      "TEST:ETHN      0.9161      0.397      2.306      0.034       0.078       1.754\n",
      "==============================================================================\n",
      "Omnibus:                        0.388   Durbin-Watson:                   3.008\n",
      "Prob(Omnibus):                  0.823   Jarque-Bera (JB):                0.514\n",
      "Skew:                           0.050   Prob(JB):                        0.773\n",
      "Kurtosis:                       2.221   Cond. No.                         5.96\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm2 = ols('JPERF ~ TEST + TEST:ETHN', data=minority_table).fit()\n",
    "print(min_lm2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/6f38J/Q/vNrqVcoXLm93NBHJBE9PlCcB84wxd1uW5QcU9PD9REREcoR1f6wj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\nHeQdzOP6pOt5pP8jNKnZxOloZ8TunN3Y6TaZL2eSvyOfWk1r0XdyX5J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gKsplFSgOsH7B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\npyNan+naigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8af2a7d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='TEST', ylabel='JPERF')\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
    "                marker=markers[factor], s=12**2)\n",
    "\n",
    "fig = abline_plot(intercept = min_lm2.params['Intercept'],\n",
    "                 slope = min_lm2.params['TEST'], ax=ax, color='purple')\n",
    "ax = fig.axes[0]\n",
    "fig = abline_plot(intercept = min_lm2.params['Intercept'],\n",
    "        slope = min_lm2.params['TEST'] + min_lm2.params['TEST:ETHN'],\n",
    "        ax=ax, color='green')\n",
    "ax.legend(['ETHN == 1', 'ETHN == 0'], scatterpoints=1, loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.572\n",
      "Model:                            OLS   Adj. R-squared:                  0.522\n",
      "Method:                 Least Squares   F-statistic:                     11.38\n",
      "Date:                Sat, 04 Feb 2017   Prob (F-statistic):           0.000731\n",
      "Time:                        17:09:35   Log-Likelihood:                -35.390\n",
      "No. Observations:                  20   AIC:                             76.78\n",
      "Df Residuals:                      17   BIC:                             79.77\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.6120      0.887      0.690      0.500      -1.260       2.483\n",
      "TEST           2.2988      0.522      4.400      0.000       1.197       3.401\n",
      "ETHN           1.0276      0.691      1.487      0.155      -0.430       2.485\n",
      "==============================================================================\n",
      "Omnibus:                        0.251   Durbin-Watson:                   3.028\n",
      "Prob(Omnibus):                  0.882   Jarque-Bera (JB):                0.437\n",
      "Skew:                          -0.059   Prob(JB):                        0.804\n",
      "Kurtosis:                       2.286   Cond. No.                         5.72\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm3 = ols('JPERF ~ TEST + ETHN', data=minority_table).fit()\n",
    "print(min_lm3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MRETUAKVdTUOMJgbbjm2Ds70zXh/8OmYFz4JXYy+5o5EJXDp8CRqVBmlfp8HR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8af8a6400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='TEST', ylabel='JPERF')\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
    "                marker=markers[factor], s=12**2)\n",
    "\n",
    "fig = abline_plot(intercept = min_lm3.params['Intercept'],\n",
    "                 slope = min_lm3.params['TEST'], ax=ax, color='purple')\n",
    "\n",
    "ax = fig.axes[0]\n",
    "fig = abline_plot(intercept = min_lm3.params['Intercept'] + min_lm3.params['ETHN'],\n",
    "        slope = min_lm3.params['TEST'], ax=ax, color='green')\n",
    "ax.legend(['ETHN == 1', 'ETHN == 0'], scatterpoints=1, loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.664\n",
      "Model:                            OLS   Adj. R-squared:                  0.601\n",
      "Method:                 Least Squares   F-statistic:                     10.55\n",
      "Date:                Sat, 04 Feb 2017   Prob (F-statistic):           0.000451\n",
      "Time:                        17:09:35   Log-Likelihood:                -32.971\n",
      "No. Observations:                  20   AIC:                             73.94\n",
      "Df Residuals:                      16   BIC:                             77.92\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.0103      1.050      1.914      0.074      -0.216       4.236\n",
      "TEST           1.3134      0.670      1.959      0.068      -0.108       2.735\n",
      "ETHN          -1.9132      1.540     -1.242      0.232      -5.179       1.352\n",
      "TEST:ETHN      1.9975      0.954      2.093      0.053      -0.026       4.021\n",
      "==============================================================================\n",
      "Omnibus:                        3.377   Durbin-Watson:                   3.015\n",
      "Prob(Omnibus):                  0.185   Jarque-Bera (JB):                1.330\n",
      "Skew:                           0.120   Prob(JB):                        0.514\n",
      "Kurtosis:                       1.760   Cond. No.                         13.8\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm4 = ols('JPERF ~ TEST * ETHN', data=minority_table).fit()\n",
    "print(min_lm4.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U1DqJACt/P1IDrUCSbk7ycCLxBv/75X80/Kgh6/au4/Nun/Of2//DNWHX2B1LLoOKsoiI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\nkQqoKIuIiIiIVOD/A6yfKwmqJ5nxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8af886ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, ylabel='JPERF', xlabel='TEST')\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
    "                marker=markers[factor], s=12**2)\n",
    "\n",
    "fig = abline_plot(intercept = min_lm4.params['Intercept'],\n",
    "                 slope = min_lm4.params['TEST'], ax=ax, color='purple')\n",
    "ax = fig.axes[0]\n",
    "fig = abline_plot(intercept = min_lm4.params['Intercept'] + min_lm4.params['ETHN'],\n",
    "        slope = min_lm4.params['TEST'] + min_lm4.params['TEST:ETHN'],\n",
    "        ax=ax, color='green')\n",
    "ax.legend(['ETHN == 1', 'ETHN == 0'], scatterpoints=1, loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Is there any effect of ETHN on slope or intercept?\n",
    "<br />\n",
    "Y ~ TEST vs. Y ~ TEST + ETHN + ETHN:TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff    ss_diff         F    Pr(>F)\n",
      "0      18.0  45.568297      0.0        NaN       NaN       NaN\n",
      "1      16.0  31.655473      2.0  13.912824  3.516061  0.054236\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1814: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    }
   ],
   "source": [
    "table5 = anova_lm(min_lm, min_lm4)\n",
    "print(table5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Is there any effect of ETHN on intercept?\n",
    "<br />\n",
    "Y ~ TEST vs. Y ~ TEST + ETHN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff   ss_diff         F    Pr(>F)\n",
      "0      18.0  45.568297      0.0       NaN       NaN       NaN\n",
      "1      17.0  40.321546      1.0  5.246751  2.212087  0.155246\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1814: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    }
   ],
   "source": [
    "table6 = anova_lm(min_lm, min_lm3)\n",
    "print(table6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Is there any effect of ETHN on slope?\n",
    "<br />\n",
    "Y ~ TEST vs. Y ~ TEST + ETHN:TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff    ss_diff         F    Pr(>F)\n",
      "0      18.0  45.568297      0.0        NaN       NaN       NaN\n",
      "1      17.0  34.707653      1.0  10.860644  5.319603  0.033949\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1814: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    }
   ],
   "source": [
    "table7 = anova_lm(min_lm, min_lm2)\n",
    "print(table7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Is it just the slope or both?\n",
    "<br />\n",
    "Y ~ TEST + ETHN:TEST vs Y ~ TEST + ETHN + ETHN:TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff  ss_diff         F    Pr(>F)\n",
      "0      17.0  34.707653      0.0      NaN       NaN       NaN\n",
      "1      16.0  31.655473      1.0  3.05218  1.542699  0.232115\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1814: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    }
   ],
   "source": [
    "table8 = anova_lm(min_lm2, min_lm4)\n",
    "print(table8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Two Way ANOVA - Kidney failure data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Weight - (1,2,3) - Level of weight gan between treatments\n",
    "Duration - (1,2) - Level of duration of treatment\n",
    "Days - Time of stay in hospital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\ipykernel\\__main__.py:5: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\pandas\\io\\parsers.py:1961: FutureWarning: split() requires a non-empty pattern match.\n",
      "  yield pat.split(line.strip())\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\pandas\\io\\parsers.py:1963: FutureWarning: split() requires a non-empty pattern match.\n",
      "  yield pat.split(line.strip())\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    kidney_table = pandas.read_table('kidney.table')\n",
    "except:\n",
    "    url = 'http://stats191.stanford.edu/data/kidney.table'\n",
    "    kidney_table = pandas.read_table(url, delimiter=\" *\")\n",
    "    kidney_table.to_csv(\"kidney.table\", sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight  Duration\n",
      "1       1           10\n",
      "        2           10\n",
      "2       1           10\n",
      "        2           10\n",
      "3       1           10\n",
      "        2           10\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Explore the dataset, it's a balanced design\n",
    "print(kidney_table.groupby(['Weight', 'Duration']).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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AggUKanFInTUREZFI8+uv0KEDjBtnlzr79YPKlYOuSgKizpqIiEikyMyEXr1sp+fXX9vm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\nkJJiZ302aRJ0RRKhMjNtJlqvXnbiQHq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d8af044898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kt = kidney_table\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = fig.add_subplot(111)\n",
    "fig = interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days']+1),\n",
    "        colors=['red', 'blue'], markers=['D','^'], ms=10, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "$$Y_{ijk} = \\mu + \\alpha_i + \\beta_j + \\left(\\alpha\\beta\\right)_{ij}+\\epsilon_{ijk}$$\n",
    "\n",
    "with \n",
    "\n",
    "$$\\epsilon_{ijk}\\sim N\\left(0,\\sigma^2\\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function anova_lm in module statsmodels.stats.anova:\n",
      "\n",
      "anova_lm(*args, **kwargs)\n",
      "    ANOVA table for one or more fitted linear models.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    args : fitted linear model results instance\n",
      "        One or more fitted linear models\n",
      "    scale : float\n",
      "        Estimate of variance, If None, will be estimated from the largest\n",
      "        model. Default is None.\n",
      "    test : str {\"F\", \"Chisq\", \"Cp\"} or None\n",
      "        Test statistics to provide. Default is \"F\".\n",
      "    typ : str or int {\"I\",\"II\",\"III\"} or {1,2,3}\n",
      "        The type of ANOVA test to perform. See notes.\n",
      "    robust : {None, \"hc0\", \"hc1\", \"hc2\", \"hc3\"}\n",
      "        Use heteroscedasticity-corrected coefficient covariance matrix.\n",
      "        If robust covariance is desired, it is recommended to use `hc3`.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    anova : DataFrame\n",
      "    A DataFrame containing.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    Model statistics are given in the order of args. Models must have\n",
      "    been fit using the formula api.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    model_results.compare_f_test, model_results.compare_lm_test\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> import statsmodels.api as sm\n",
      "    >>> from statsmodels.formula.api import ols\n",
      "    >>> moore = sm.datasets.get_rdataset(\"Moore\", \"car\", cache=True) # load\n",
      "    >>> data = moore.data\n",
      "    >>> data = data.rename(columns={\"partner.status\" :\n",
      "    ...                             \"partner_status\"}) # make name pythonic\n",
      "    >>> moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_status, Sum)',\n",
      "    ...                 data=data).fit()\n",
      "    >>> table = sm.stats.anova_lm(moore_lm, typ=2) # Type 2 ANOVA DataFrame\n",
      "    >>> print(table)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(anova_lm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Things available in the calling namespace are available in the formula evaluation namespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', data=kt).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "ANOVA Type-I Sum of Squares\n",
    "<br /><br />\n",
    "SS(A) for factor A. <br />\n",
    "SS(B|A) for factor B. <br />\n",
    "SS(AB|B, A) for interaction AB. <br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         df     sum_sq   mean_sq          F    PR(>F)\n",
      "C(Duration)             1.0   2.339693  2.339693   4.358293  0.041562\n",
      "C(Weight)               2.0  16.971291  8.485645  15.806745  0.000004\n",
      "C(Duration):C(Weight)   2.0   0.635658  0.317829   0.592040  0.556748\n",
      "Residual               54.0  28.989198  0.536837        NaN       NaN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:875: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "C:\\Programs\\WinPython-64bit-3.6.0.1Qt5\\python-3.6.0.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1814: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    }
   ],
   "source": [
    "print(anova_lm(kidney_lm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "ANOVA Type-II Sum of Squares\n",
    "<br /><br />\n",
    "SS(A|B) for factor A. <br />\n",
    "SS(B|A) for factor B. <br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          sum_sq    df          F    PR(>F)\n",
      "C(Duration)             2.339693   1.0   4.358293  0.041562\n",
      "C(Weight)              16.971291   2.0  15.806745  0.000004\n",
      "C(Duration):C(Weight)   0.635658   2.0   0.592040  0.556748\n",
      "Residual               28.989198  54.0        NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "print(anova_lm(kidney_lm, typ=2))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "ANOVA Type-III Sum of Squares\n",
    "<br /><br />\n",
    "SS(A|B, AB) for factor A. <br />\n",
    "SS(B|A, AB) for factor B. <br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                      sum_sq    df           F        PR(>F)\n",
      "Intercept                         156.301830   1.0  291.153237  2.077589e-23\n",
      "C(Duration, Sum)                    2.339693   1.0    4.358293  4.156170e-02\n",
      "C(Weight, Poly)                    16.971291   2.0   15.806745  3.944502e-06\n",
      "C(Duration, Sum):C(Weight, Poly)    0.635658   2.0    0.592040  5.567479e-01\n",
      "Residual                           28.989198  54.0         NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "print(anova_lm(ols('np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Poly)', \n",
    "                   data=kt).fit(), typ=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Excercise: Find the 'best' model for the kidney failure dataset"
   ]
  }
 ],
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